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LIQUIDITY LINKAGES BETWEEN THE SOUTH
AFRICAN BOND AND EQUITY MARKETS
S.C. MAGAGULA
212450204
2014
i
LIQUIDITY LINKAGES BETWEEN THE SOUTH
AFRICAN BOND AND EQUITY MARKETS
By
SIFISO CHARLES MAGAGULA
Submitted in fulfilment of the requirements for MCom
(Economics-Research) at the Nelson Mandela
Metropolitan University
October 2014
Promoter/Supervisor: Prof Matthew K. Ocran
Co-Promoter/Co-Supervisor: Prof E. Gilbert
ii
DECLARATION BY CANDIDATE
I, Sifiso Charles Magagula, student no:212450204, hereby declare
that the treatise/ dissertation/ thesis for MCom in Economics to be
awarded is my own work and that it has not previously been
submitted for assessment or completion of any postgraduate
qualification to another University or for another qualification.
Sifiso Charles Magagula
iii
ABSTRACT
Purpose - The study sought to examine the liquidity linkages between the South African
bond and equity markets before the global financial crisis in 2008.
Design/methodology/approach: The window of observation covered the period January 2000
to September 2008. In order to ensure robustness in the estimation, the study used foreign
participation in the various markets as an additional measure of liquidity. The other liquidity
measures considered in the study were volume and value traded of the various securities
respectively. Time series modeling techniques were used in the estimation. An unrestricted
vector autoregressive (VAR) model was estimated following which the standard innovation
accounting techniques, impulse response functions and forecast error variance
decompositions were applied. In the empirical analysis, the Granger-causality between the
two markets was also used.
Findings - While all the liquidity measures suggest the existence of linkages between the
bond and equity markets, the direction of causality was found to be unidirectional from equity
to the bond market using the volume and value measures. On the other hand, the foreign
participation measure of liquidity suggests bi-directional causality. The study also provides
evidence of long run relationship between key macroeconomic variables such as inflation,
exchange rate and interest rate on one hand and liquidity in the debt and equity markets on
the other. As empirical findings indicates that the linkages in liquidity between these markets
positive, this consistent with studies conducted by Chordia et al (2003 & 2005) and Engsted
and Tanggaard (2000) who found the relationship was a positive one. When volumes of
trade and trade values, the study find evidence on uni-directional causality and strong bi-
directional causality is evidence when foreign investor participation is used as a liquidity
measure. In summary, there is a strong evidence liquidity linkage between the bond and
equity market from the empirical results.
Keywords: South Africa, bond markets, equity markets, liquidity.
iv
ACKNOWLEDGEMENTS
I wish to thank my supervisor, Professor Matthew Ocran for his constant supervision,
intellectual support and continual encouragement when I was writing this paper. This
thesis was made possible by his patience and persistence. I thank you for your support
and endless patience and believe that you‘ve shown to me and for that; I will always be
eternally grateful and indebted for your love and support.
I would also like to thank Mr Forget Kapingura (fellow student whilst I was at the
University of Fort Hare) for always helping me with the empirical work. I cannot quantify
your assistance in numbers but I am grateful for what you have done for me and your
help has never gone unnoticed for the beginning. May the Lord our Gold bless you.
To the most important to us all, the Lord our God (Almighty), for it is only few who
receives from the divine hand and the intellectual ability and huge support structures and
surroundings to accomplish a Master‘s Degree. He‘s the Mighty God and I humbly thank
Him.
v
TABLE OF CONTENTS
DECLARATION BY CANDIDATE ............................................................................................ ii
ABSTRACT .............................................................................................................................. iii
ACKNOWLEDGEMENTS ........................................................................................................ iv
CHAPTER 1 ............................................................................................................................... 1
INTRODUCTION ....................................................................................................................... 1
1.1. Background and problem statement ................................................................................. 1
1.2. Objective of the study .......................................................................................................... 2
1.3. Significance of the study ..................................................................................................... 2
1.4. Outline of the study .............................................................................................................. 3
CHAPTER 2 ............................................................................................................................... 4
OVERVIEW OF THE SOUTH AFRICAN BOND AND EQUITY MARKETS ........................... 4
2.1. Introduction ............................................................................................................................ 4
2.2. Overview of the South African Bond Market .................................................................... 5
2.2.1. Early development; Over the Counter (OTC) and primary market ............... 6
2.2.2. Structural improvements and the inception of a secondary bond market. .. 8
2.2.3. Bond Exchange of South Africa: a formal and sophisticated market ......... 10
2.2.4. Recent developments ........................................................................................ 11
2.2.5. BESA market structure ...................................................................................... 13
2.2.6. Size and performance of the bond market ..................................................... 15
2.2.7. Listing requirements ........................................................................................... 26
2.2.8. Trading, Clearing and Settlements .................................................................. 27
2.3. Overview of the South African Equity Market and the JSE ......................................... 29
2.3.1. Early development and structural improvements of the equity markets .... 30
2.3.2. Recent developments ........................................................................................ 35
2.3.3. Size and the performance of the South African Equity market ................... 35
2.3.4. Listing requirements ........................................................................................... 44
2.3.5. Trading, clearing and settlements .................................................................... 45
2.3.6. South African Financial Market regulations .................................................... 47
2.3.7. Introduction of the Financial Markets Bill ........................................................ 48
2.4. Summary of the chapter .................................................................................................... 50
CHAPTER 3 ............................................................................................................................. 52
LITERATURE REVIEW ........................................................................................................... 52
vi
3.1. Introduction .......................................................................................................................... 52
3.2. Definitions ............................................................................................................................ 52
3.3. Theoretical literature .......................................................................................................... 54
3.3.1. Assets pricing theory .......................................................................................... 54
3.3.2. Market liquidity and investor sentiments ......................................................... 55
3.3.3. Clientele effects and liquidity policies .............................................................. 56
3.3.4. The Duffie, Gârleanu, and Pedersen (DGP) Model ...................................... 57
3.4. Empirical literature review ................................................................................................. 58
3.4.1. Bond and equity market liquidity- market to market linkages ...................... 58
3.4.2. Cross country liquidity linkages between the bond and equity market ...... 63
3.4.3. Multiple-Markets liquidity linkages ................................................................... 68
3.4.4. Other liquidity measures and linkages across markets ................................ 73
3.5. Conclusion ........................................................................................................................... 76
CHAPTER 4 ............................................................................................................................. 78
METHODOLOGY .................................................................................................................... 78
4.1. Introduction .......................................................................................................................... 78
4.2. Theoretical framework ....................................................................................................... 78
4.3. Empirical Model specification ........................................................................................... 79
4.3.1. Stationarity Analysis ........................................................................................... 80
4.3.2. Multivariate vector autoregression and Johansen‘s Cointegration test ..... 81
4.3.3. Generalised Impulse response function and error variance decomposition
84
4.3.4. Granger causality test ........................................................................................ 85
4.4. Definition of variables and sources of data .................................................................... 85
CHAPTER 5 ............................................................................................................................. 87
ESTIMATION AND INTERPRETATION OF THE RESULTS ................................................ 87
5.1. Introduction ......................................................................................................................... 87
5.2. Unit root testing ................................................................................................................... 87
5.3. Lag Length Selection Criteria ............................................................................................ 89
5.3.1. Lag Length Selection Criteria- Liquidity: Volumes of trade model .............. 89
5.3.2. Lag Length Selection Criteria- Liquidity: Trade Values model ................... 90
5.3.3. Lag Length Selection Criteria- Liquidity: Foreign Investor Participation
model 91
5.4. Johansen Cointegration Test............................................................................................ 92
5.5. Correlation matrixes ........................................................................................................... 97
vii
5.6. Impulse Response Function ............................................................................................. 99
5.6.1. Impulse Response Function- Liquidity: Volumes of trade model ................ 99
5.6.2. Impulse Response Function- Liquidity: Trade Values model .................... 102
5.6.3. Impulse Response Function- Liquidity: Foreign Investor Participation
model 104
5.7. Variance Decomposition ................................................................................................. 106
5.7.1. Variance Decomposition- Liquidity: Volumes of trade model .................... 106
5.7.2. Variance Decomposition:- Liquidity: Trade Values model ......................... 107
5.7.3. Variance Decomposition- Liquidity: Foreign Investor Participation model
108
5.8. Diagnostic Checks ........................................................................................................... 110
5.9. Granger Causality Test ................................................................................................... 113
5.10. Summary- Liquidity: Volumes of trade model, Trade values model and Foreign
Investor Participation model ....................................................................................................... 114
CHAPTER 6 ........................................................................................................................... 117
CONCLUSIONS AND RECOMMENDATIONS .................................................................... 117
6.1. Summary of the study and conclusions ............................................................................ 117
6.2. Policy implications and recommendations ................................................................... 119
6.3. Limitations of the study .................................................................................................... 120
7. REFERENCES .................................................................................................................. 121
8. APPENDICES ........................................................................................................................ a
viii
LIST OF FIGURES
Figure 2.1: BESA Market Structure ....................................................................................................... 14
Figure 2.2: Liquidity in the government bond markets ........................................................................ 17
Figure 2.3: Turnover on domestic and International bond exchanges (1995-2010) ............................ 19
Figure 2.4: Government bond yields..................................................................................................... 20
Figure 2.5: BESA Markets Trade by Sector Q3/2011 (%) ...................................................................... 21
Figure 2.6: Monthly foreign participation and R208 bond yield 2012 .................................................. 22
Figure 2.7: Domestic Government bonds ownership 31 December 2010 ........................................... 23
Figure 2.8: Market Depth (2000-2008) ................................................................................................. 24
Figure 2.9: Securitisation (%) of growth listing 2008 ............................................................................ 25
Figure 2.10: JSE All Share Index Performance (2002-2012) .................................................................. 37
Figure 2.11: Earnings yield on JSE shares .............................................................................................. 38
Figure 2.12: Dividend yield ................................................................................................................... 38
Figure 2.13: Market Capitalisation (R’ Billion: 1975-2011) ................................................................... 41
Figure 2.14 JSE Equities Volumes traded 2000-2011 (R’ Billion) .......................................................... 42
Figure 2.15 JSE equity markets foreign participation ........................................................................... 43
Figure 2.16: JSE share prices ................................................................................................................. 44
Figure 5.6.1: Impulse Response Function: Volumes of trade model .................................................. 101
Figure 5.6.2: Impulse Response Function: Trade Values model ......................................................... 103
Figure 5.6.3: Impulse Response Function: Foreign Investor Participation model .............................. 105
Figure 5.8.1 AR Roots Graph- Liquidity: Volumes of trade model ...................................................... 110
Figure 5.8.2 AR Roots Graph- Liquidity: Trade values model ............................................................. 111
Figure 5.8.3 AR Roots Graph- Liquidity: Foreign Investor Participation model .................................. 112
ix
LIST OF TABLES
Table 2.1: Size of the securities market at the end of 2006 (Billions US dollars) ................................. 15
Table 2.2: S.A Bond Markets Performance ........................................................................................... 16
Table 2.3: Growth in the S.A Financial Markets .................................................................................... 18
Table 5.1 Unit Root Test- ADF (Variables in Levels and First Difference) ............................................. 88
Table 5.2: Unit Root Test Phillips- Perron (Variables in Levels and First Difference) ........................... 88
Table 5.3.1: VAR Lag Order Selection Criteria -Liquidity: Volumes of trade model ............................. 90
Table 5.3.2: VAR Lag Order Selection Criteria- Liquidity: Trade Values model ................................... 91
Table 5.3.3: VAR Lag Order Selection Criteria- Liquidity: Foreign Investor Participation model ........ 92
Table 5.4.1: Johansen Cointegration Test results- Liquidity: Volumes of trade model ........................ 92
Table 5.4.2: Error Correction Model Results- Liquidity: Volumes of trade model ............................... 94
Table 5.4.3: Johansen Cointegration Test results- Liquidity: Trade Values model .............................. 94
Table 5.4.4: Error Correction Model Results- Liquidity: Trade Values model ...................................... 95
Table 5.4.5: Johansen Cointegration Test results- Liquidity: Foreign Investor participation model .... 96
Table 5.4.6: Error Correction Model Results- Liquidity: Foreign Investor participation model ........... 97
Table 5.5.1: Correlation Matrix- Liquidity: Volumes of trade model .................................................... 97
Table 5.5.2: Correlation Matrix- Liquidity: Trade Values model .......................................................... 98
Table 5.5.3: Correlation Matrix- Liquidity: Foreign Investor Participation model ................................ 99
Table 5.7.1: Variance Decomposition Results- Liquidity: volumes of trade model ............................ 106
Table 5.7.2: Variance Decomposition Results: Trade Values model .................................................. 107
Table 5.7.3: Variance Decomposition Results: Foreign Investor Participation model........................ 109
Table 5.9.1: Granger Causality Test results- Liquidity: Volumes of trade model ................................ 113
Table 5.9.2: Granger Causality Test results- Liquidity: Trade values model ....................................... 113
Table 5.9.3: Granger Causality Test- Liquidity: Foreign Investor Participation model ....................... 114
x
LIST OF ACRONYMS
AACB: Association of African Central Banks
ALBI: All Bond Index
AltX: Alternative Exchange Board
ATS: Automated Trading System
BDA: Broker Deal Accounting
BESA: Bond Exchange of South Africa
BIS: Bank of International Settlement
BMA: Bond Market Association
BTA: Bond Trader‘s associations
CDS: Central Security Depository
CFC: Customer foreign currency
CGFS: Committee on Global Financial System
CISCA: Collective Investment Scheme Control Act
CPA: Consumer Protection Act
CSD: Central Security Depository
CSD: Central Security depository
DIA: Debt Issuers Association
DIA: Debt Issuers Associations
DTA: Derivatives Trader‘s Association
DTI: Department of Trade and Industry
ETF: Exchange traded Funds
ETFs: Exchange Traded Funds
FIPB: Foreign Investor Participation in Bonds
FIPE: Foreign Investor Participation in Equities
FMA: Financial Markets Control Act
xi
FMB: Financial Markets Bill
FMCA: Financial Markets Control Act
FRA: Forward Rate Agreement
FSB: Financial Services Board
FSB: Financial Services Board
GDP: Gross Domestic Product
GLA: General Loans Act
GOVI: Government Bond Index
IDBs: Inter Dealer Brokers
ITA: Insider Trading Act
IV: Information Vendor
JET: Johannesburg Equity Trading
JSE: Johannesburg Stock Exchange
LSE: London Stock Exchange
MA: Market Association
MMB: Money Markets Bill
MTBPS: Medium Term Budget Policy Statement
NT: National Treasury
OTC: Over-the- Counter
OTHI: Other Bond Index
PDC: Public Debt Commissioners
SA: South Africa
SADC: Southern African Development Community
SAFEX: South African Future Exchange
SAFIRES: South African Financial Instruments Real time Electronic Settlement System
SAMOS: South African Multiple Option Settlement System
xii
SARB: South African Reserve Bank
SECA: Stock Exchange Control Act
SENS: Securities Exchange News
SRO: Self-Regulatory Organisation
SSA: Security Services Act
Strips: Separate Trading of Registered Interest and Principal Securities
TBs: Treasury Bills
TVB: Trade Values in Bonds
TVE: Trade Values in Equities
UNEXcor: Universal Exchange Corporations
VOLB: Volumes of Bonds
VOLE: Volumes of Equities
WEF: World Economic Forum
WFE: World Federation of Exchanges
WFE: World Federation of Exchanges
1
CHAPTER 1
INTRODUCTION
1.1. Background and problem statement
The South African debt and equity markets have experienced enormous growth over the few
past decades. Ambrosi (2009: 5) describes the South African bond markets as constituting
the lion‘s share of the African debt market. According to Ambrosi (2009:5), it boasts
sophistication and efficiency that match those of many of the bigger debt markets in the
developed markets of the world and she referred to it as both a David and a Goliath in which
in its former role, it is often a taker of global financial market developments that from time to
time ripple out globally, while in its latter role it is a leader on the continent, and possibly
even among emerging markets elsewhere. At the end of 2009, the South African bond
market had a market capitalization value of US$139, 5 billion (ZAR 1 028.2 billion). In terms
of turnover, in the same period, it amounted to US$1.8 trillion while in 2008 the bond market
registered a record turnover of US$2, 1 trillion which was attributed to a surge in trading
during the height of the global financial crisis.
The Johannesburg Stock Exchange (JSE) is the oldest stock exchange in Sub-Saharan
Africa. It was established in 1887 and over the years, it has undergone a series of
transformations and restructuring activities. The increasing role of stock markets in economic
development has now been recognised, since the advent of democracy in the early 1990s,
South Africa embarked on a wide range of financial reforms both in the banking sector and
stock market system. In 2008, South African financial system was ranked 25th in the world
by the World Economic Forum‘s first financial development index. In 2008, South Africa was
ranked ahead of India, Brazil and Russia and this according to Ndako (2010:3) ―....led to
South Africa being included in the major global stock market indices‖. The South African
financial system is regarded as being fundamentally sound with a good legal framework and
sound financial infrastructure supported by prudent macroeconomic management.
Beside the fact that the South African financial sector is sound, little has been done in
analysing the co-movements and linkages of liquidity across the different markets. Chordia
et al (2001:3) analysed common determinants of bond and stock market liquidity. Chordia et
al argued that previously documented autocorrelation in liquidity changes raises questions
related to practical and scientific issue of whether future liquidity is predictable from publicly
available information.
2
Over the last two decades, academic interest has been broad regarding the properties of
market liquidity and its importance on the functioning of markets. In many studies conducted
about market liquidity, mostly, it has been found that there are remarkable commonalities in
market liquidity. Nikolaou (2009:15) also found that ―there is a positive covariance between
individual stock liquidity and overall market liquidity‖. Nikolaou‘s findings are however
consistent with work by Chordia et al. (2000, 2003) who have also have documented that
liquidity is correlated across markets, namely across stocks in different markets and across
stocks and bonds. After observing that there is indeed commonality in liquidity, Chordia et al
(2000) argued that ―....a wider-angle lens exposes an imposing image of commonality,
meaning that, quoted spreads, quoted depth, and effective spreads co-move with market
and industry-wide liquidity (2000:3). The authors also highlighted that, after controlling for
well-known individual liquidity determinants, such as volatility, volume, and price, common
influences remain significant and material. When studying the South African financial
markets, the question that emerges is; what has been the trend and performance of the
South African bond and equity markets over the years? An associated question is, does the
South African equity and bond market have any commonalities is considered in the light of
the above discussion?
1.2. Objective of the study
The main objective of this study therefore, is to identify the liquidity linkages between the
South African bond and equity markets. However, the specific objectives are:
i. To examine the trends in the performance and liquidity of the South African equity
and bond markets between the period 2000 to 2008;
ii. To empirically examine the extent to which equity and bond market liquidity responds
to the same underlying fundamentals;
iii. Based on the empirical results, articulate the policy implications of the study for the
growth of the South African equity and bond market and the overall economy.
1.3. Significance of the study
Stocks and bonds are important for resource allocation, as they are the main vehicles by
which funds are raised for long-term investments by firms and governments (Chordia et al:
2001:3). Since liquidity has been shown to be related to asset returns and the costs of
capital, analysing how stock and bond liquidities move and co-move over time is important
for enhancing the efficacy of resource allocation in the South African financial markets.
3
Chordia et al (2003:2) argued that there is good reason to believe that liquidity in the stock
and bond markets covaries, however, referring to a number of scholarly articles such
Campbell and Ammer (1993), Fleming, Kirby and Ostdiek (1998), Ho ad Stoll (1983) and
O‘Hara and Oldfield (1986) they acknowledge that although the unconditional correlation
between stock and bond returns is low, there are strong volatility linkages between the two
markets, which can affect liquidity in both markets by altering the inventory risk borne by
market making. It is also argued that stock and bond market liquidity may interact via trading
activity. However, a number of asset allocation strategies shift wealth between stock and
bond markets and according to Chordia et al, ―....a negative information shock in stocks
often causes a ―flight to quality‖ as investors substitute safe assets for risky assets‖ (2003:2).
The shift from the now risky stocks into bonds may cause price pressures and also impact
stock and bond liquidity.
It is evident from the background to the study that the linkages between bond equity market
liquidity for emerging markets remains inconclusive, again there are few studies that focuses
on emerging economies. This is at the empirical level. Establishing whether there is a
linkage of liquidity in the South African debt market and equity markets will help address
questions like, how market participation can be improved in these important markets for
raising long term capital finance. These analyses will also enhance in providing a clear
understanding of the dynamic behaviour of liquidity and using South African data would
provide a clearer view that may help policy makers in planning decisions for these markets
for the benefit of the country‘s economy.
1.4. Outline of the study
The study is divided into six chapters. Chapter 1 provides the introduction and objective of
the study. Chapter 2 focuses on the overview of the South African equity and bond markets
with emphasis on the recent development and trend, functions, characteristics and
description of both the equity and bond markets. Chapter 3 is the theoretical and empirical
literature. Chapter 4 of the study focuses mainly on theoretical framework and model
specification. Chapter 5 presents results of the empirical analysis. Chapter 6 will highlight on
the results as well main conclusions.
4
CHAPTER 2
OVERVIEW OF THE SOUTH AFRICAN BOND AND EQUITY MARKETS
2.1. Introduction
Financial markets play a critical role in mobilising savings towards investment in households,
businesses and government, in order to support their sustained growth and development.
This is made possible through channelling capital from those who can supply it to those who
need it. In addition to raising capital, these individuals and entities use the financial markets
to manage their risk and invest their savings to ensure future prosperity (Financial Market Bill
2011:4). The SA capital markets therefore play a pivotal role in allocating domestic and
foreign savings towards South African investment requirements. This happens, whether
directly through trading on the market or indirectly through another investment product like a
collective investment scheme, through the listed and unlisted bond and equity markets
including both the spot and derivatives markets.
Financial markets in most African countries are shallow, and have inadequate access to
finance. Consequently, mobilization of domestic resources as an alternative source of
financing has been more important in most of the African countries over the past few
decades, with government in most African countries focusing on domestic markets in order
to avoid renewed or unsustainable external indebtedness as well as other restrictions that
international markets have. Easy access to concessional financing had reduced the need to
develop domestic bond markets in many SSA countries. In acquiring capital finances, most
of the African countries are still reliant on external donor funds, predominantly in the form of
multilateral and bilateral loans and grants secured on concessional terms. However, this is
not the case in South Africa as there exist a mature bond and equity markets and they are
well developed and liquid relative to other developing economies. This is also more
elaborated in the work of Adelegan and Radzewicz-Bak (2009:3), they asserted that
―....despite a long history of fiscal deficits and a growing need for developmental and
structural investments, with the one exception of South Africa, bond markets in SSA have
remained shallow, illiquid, and inefficient‖. This section will focus on the development trends
of the South African bond and equity markets relative to other bond and equity markets of
the world.
5
2.2. Overview of the South African Bond Market
Since its inception and early development, the South African bond market has undergone
major developments and shifts. This ranges from the methods used, participants, regulation,
policies and other developed relevant legislations that contributed to the development of the
market as it is today. All the relevant factors that contributed to its development resulted in
enhanced efficiency and safety in the market, thus attracting investors to it. As highlighted in
Ambrosi (2010:5) that, the ―....South Africa‘s debt market when measured in terms of debt
issued comprises but a fraction of the world‘s debt markets combined, yet it constitutes the
lion‘s share of the African debt market, boasting sophistication and efficiency that match
those of many of the bigger debt markets in the developed world‖.
Although the South African bond market is well developed, it is imperative to mention that,
the development of a country‘s bond market is crucial in that, it serves as an alternative
source for debt financing. A well-developed bond market reduces the over-reliance on bank
lending for debt financing and minimises exposure of the economy to the risk of a failure in
the banking system and this has been evident in the recent financial crisis as the South
African banks were not heavily affected hence the South African bond market was argued to
be the most stable one during the crisis period. A well-developed bond market also lowers
the cost of capital financing, and provide for portfolio diversification opportunities across the
assets classes. In case of government; an established government bond market facilitates
the existence of benchmark yields curve, an important element that aides a conducive
environment for an active and liquid market. As in Hove (2008:10) bond markets reduce
financing costs through disintermediation and an active and efficient bond market would
broaden capital markets by offering investors opportunities to invest in a wider range of
assets. Hove further asserted that ―....the existence of a well-functioning bond market can
lead to the efficient pricing of credit risk, since expectations of all bond market participants
are incorporated into bond prices and the market supports the economy in meeting its
financing needs during periods of rapid economic growth‖ (Hove: 2008:10).
In analysing the trends in the growth of the South African bond market, this section will look
briefly at the history and development, structure, performance, trading functions and listing
requirements of the South African bond market as well as the roles played by the South
African Reserve Bank (SARB) and the National Treasury in moulding the bond market. This
section will follow and benefit mostly from the work of Hove (2008) and Kapingura and Ikhide
(2011), in that the work of both authors is organised in a manner that depicts the important
picture of the South African bond market. However, this study will differ slightly and add to
6
the mention papers since it will use recently available data in terms comparisons and
performance.
This section will focus mainly on the Bond Exchange of South Africa (BESA) since the South
African bond market is an exchange driven market. BESA is an independent financial
exchange licensed in terms of the Securities Services Act, 2004 (SSA). It regulates the
trading, clearing and settlement of inter alia bonds, bond futures, vanilla swaps, forward-rate
agreements (FRAs) and bond options. It is a self-regulatory organisation operating under an
annual licence granted by the country‘s securities market regulator, the Financial Services
Board (FSB).
BESA is the regulator of the listing and trading in interest-rate securities (bonds and
derivatives) in accordance with the Securities Services Act, 2004 and its own Rules and
Directives. BESA seeks to promote bond market liquidity by providing a range of services to
authorised users, to issuers, traders and investors alike. As at 31 December 2007, BESA
reported 967 listed securities, issued by 104 issuers, with a total market capitalisation of
R863 billion. The South African market remains one of the most liquid markets in the world
with the recorded velocity for 2007 of 17 times its market capitalisation.
2.2.1. Early development; Over the Counter (OTC) and primary market
The development of bond markets must be seen as a continuous process in which continued
macroeconomic and political stability are essential to building an efficient market and
establishing the credibility of the government as an issuer of debt securities (Hove:2008:20).
The SA bond market is argued to have started with the first issue of long-term paper in the
first colony of the Republic of South Africa (Faure 2006:158). The first attempt to issue a
debt instruments is argued to have taken place in 1820 in the colony of the Cape of Good
Hope when the then Governor, Lord Charles Somerset issued a Proclamation for the issue
of debentures and this move is argued to have been an unsuccessful one and resulted in the
Governor obtaining a loan from the British Government (Faure: 2006:159). At the time, loans
were mainly utilised in the subsequent years and the first issue of debenture took place in
1857. The Natal Colony, the Orange Free State Republic and the Transvaal Republic
followed in afterwards with their own issues. According to Faure (2009), the Public long-term
paper was first issued in 1843, and in 1861 municipal bonds appeared.
7
After the passing of the 1911 General Loans Act, the first issue of loan stock (bonds)
appeared, which provided for the loan procedures of the Central Government. According to
Faure, bonds were first issued for public tenders or directly to investors, and as from 1917,
subscriptions for bonds were invited. This remained the main method of issuing bonds by the
government, municipal authorities as well as public corporations until the early eighties
(Faure: 2006:159). Treasury was the only one determining the main rates on issue.
As from 1982, treasury, in addition to issuing bonds on subscription, began issuing bonds on
tender basis via its agent, the Reserve Bank. Later the same year, the Reserve Bank began
to issue bonds on a tap basis, and most other large bond issuers adopted and followed this
method. In the late nineties (90‘s) the National Treasury appointed market makers called
primary dealers to take up and make market in government bonds (Faure: 2006:159)
The South African authorities are applauded for the role that they have played in the
development of the South African bond market in the last four decades. Some of the
important phases of development is argued to have been taken in the 1970s, when the
South African bond market was predominantly an over-the-counter (OTC) or non-exchange
traded market (Faure: 2006:77). These mean that whenever a bond was bought or sold, a
physical contract passed between the respective parties the next day (Hove: 2008:37). At
that time, the bond market was dominated by government and quasi-government as debt
issuers in the bond market. The government issued bonds at par, on demand, on an open-
ended tap basis (Hove: 2008:37). Hove further asserted that, at that time, there was no
benchmark government yield curve and price discovery was limited and inefficient.
This resulted in many of the issuers in the 1980s making markets in their own bonds and
trading actively in their own bonds in order to enhance marketability. This situation has been
described as unique to South Africa as in other countries issuers play no part in the market
making of their own bonds as this task is fulfilled by appointed banks (Faure 2006:159).
The financial Market Control Act (FMCA) 55 of 1989 paved the way for the formalisation of
the bond market in South Africa (Faure: 2006:77). According to Hove (2008:38), later,
trading took place on the trading floor of the Johannesburg Stock Exchange (as it was
known at the time) through ―open outcry‖. Not only were there no real benchmarks, there
was also very little transparency in this relatively informal market. Regarding the role of the
SARB in this first phase of development, there was no clear distinction between monetary
and fiscal policies: primary issues were used for both financing government spending and
open-market transactions (AACB, 2006
8
2.2.2. Structural improvements and the inception of a secondary bond market
Faure (2006:159) points out that before the 1950s, the secondary bond market was
fundamentally absent. This complicated government initiatives in making new issues. The
Reserve Bank and the Treasury introduced the ―pattern of rates‖ in July 1952 which was a
list of all government bonds in issue and the rates at which the Reserve Bank and the Public
Debt Commissioners (now the PIC) were prepared to buy and sell these securities. This
initiative was meant to create stable and orderly conditions in the bond market and it
succeeded in adding some impetus to the market. With developments in the money market
in the early 1950s, activities in short-term government bonds emerged. The market in short-
term bonds was considered sufficiently developed by the mid-1950s, resulting in the
abolishment of the pattern of rates on those bonds in May 1995 (Faure 2006:160).
As government took an initiative to improve the structure of the primary bond market, the
SARB played an active role in developing the secondary bond market. In 1990, the SARB
commenced market making by quoting firm two-way prices in a number of benchmark
government bonds and this initiative was specifically aimed at improving the efficiency of the
secondary market, which, at that time, was still relatively weak (Hove: 2008: 38&39). The
SARB also increased its minimum trade amount from initially R1 million to R10 million in
1995, and raised the spread between the buying and selling yield from two to three basis
points. During this period, the SARB‘s market making transactions increased substantially,
and at its peak represented approximately 30% of total turnover in the secondary bond
market (Hove: 2008: 38&39).
As the funding agent of government, the South African Reserve Bank become a net seller of
government bonds, even in adverse market conditions, by becoming a leading player in the
trading of bond derivatives. It would typically be a buyer of put options and a seller of call
options, which facilitated its funding responsibilities during bear markets when investor
demand for bonds diminished. These market-making activities of the SARB contributed to an
improved investment rating for government bonds and allowed government to borrow at
relatively lower rates (AACB, 2006: 5).
In a different light, the development of the market was enhanced by the establishment of the
first merchant bank in 1955 and the first two discount houses in 1957 and 1961 respectively.
Also, the promulgation of the 1965 Banks Act and the introduction of severe liquid asset
requirements for which short term government bonds qualified resulted in the market
becoming more active. In addition, the discount houses began publishing price lists for the
9
various short-term bonds in issue. Although the market became more active, it also became
narrower. This was due to fact that, the liquid asset requirements for banks caused the rate
for liquid bonds to be lower than for other securities of equivalent maturity, rendering them
unattractive for long-term insurers and pension funds. These institutions‘ interest in these
bonds was restricted to selling them to the banks as they became liquid (ACCB 2006). The
opening of the Reserve Bank‘s money market desk in 1973 and its decision to increase its
activities in the open market for bonds further added impetus to the short-term government
bond market. Also, the same period witnessed many banks establishing Treasury Divisions
amongst other things to trade in short-term government bonds (Faure 2006). With all these
developments, in the early 1970s activity in the secondary long-term bond market also
emerged which led to the abolishment of the pattern of rates on long-term bonds in 1975.
The passing of Article 19(2) of the Exchequer and Audit Act 1975 in 1976 was another
important milestone. This enabled the Reserve Bank to request special issues of
government bonds (Faure: 2006:160).
In the mid-seventies, there was a rapid increase in government‘s budget deficit and therefore
the supply of bonds began to increase rapidly. Stock broking firms began exploring
opportunities in the bond market which was mainly dominated by the discount houses (Faure
2006:160). In the 1980s a number of initiatives were undertaken to enhance the
development of the secondary bond market. Amongst them were, allowing issuers to make
markets in their own bonds in order to enhance their marketability and reduce their cost of
funding. The other factor was the abolition of prescribed investment requirements for banks
in 1985. This resulted in banks becoming more active as dealers in bonds. Also the
emergence of the options on bonds market in the same period which allowed investors to
reduce risks they were exposed to and the subsequent maturation of this market also played
a role in increasing turnover in bonds (ACCB 2006). In the early 1990s, the Reserve Bank in
a bid to increase the marketability of government bonds undertook a decision to act as a
market maker in the respective securities. This was argued as one of the major reasons for
the increase in turnover recorded in 1992 and 1993 (Faure: 2006:161).
Another important factor which improved the development of the secondary bond market is
the implementation of the electronic settlement in 1995. This increased efficiency and safety
of the market and reduced the risk of tainted scripts being introduced into the market. Prior
to this, settlement took place in physical form, when cheques and certificates were
exchanged every Thursday, which was a fixed 10 working days after a trade was struck
(Faure: 2006:161). The appointment of 12 banks, both local and foreign banks as primary
dealer market makers in benchmark government bonds in April 1998 by the National
10
Treasury was another milestone in developing the secondary bond market in South Africa
(Faure: 2006:162). As at the end of March 2011, there were only six banks that remained as
primary dealers on government bonds and they remain the only active players in the primary
government spot bond. Prior to the selection of the banks as primary dealers, the South
African bond market was fragmented and illiquid and this role was left to the Reserve Bank.
The selection of the banks was based on a set of criteria to ensure that such primary dealers
would have the capacity to deal with inherent risks associated with market making (AACB
2006:6). This resulted in significant increase in liquidity in the market.
2.2.3. Bond Exchange of South Africa: a formal and sophisticated market
In a bid to minimise the risks associated with OTC markets, in the late eighties, the
frontrunner to BESA, the Bond Market Association (MBA), was formed at this time and was
transmuted into a fully licensed exchange in 1996 in terms of the Financial Markets Control
Act (FMCA) which was later replaced by the Security Services Act No.36 0f 2004 (SSA)
(Faure: 2006:78). As liquidity improved in the bond market in the late 1980s, this attracted
much participants resulting in huge sums of money circulating in the market. It became
necessary to regulate such huge sums of money. A government commission of enquiry (the
Stals/Jacobs Report) explored the matter and concluded that the market should be regulated
either by the South African Reserve Bank or market participants themselves (Faure:
2006:162). The market chose self-regulation and in 1989, most bond-trading firms voluntarily
formed the Bond Market Association (BMA). The report also recommended tight control
through regulation in the financial markets under the umbrella of the Financial Markets
Control Act (FMCA), (BESA 2004).
However, in 1996 the BMA was formally licensed by the Registrar of Financial Markets and
renamed the Bond Exchange of South Africa (BESA). Its prime responsibility, in terms of its
license, was to regulate South Africa‘s bond market by imposing a range of requirements for
the listing of debt instruments and for the entry of firms to membership of BESA and their
conduct thereafter (BESA 2004). The specific objectives included: addressing the key
systemic risks inherent in the market; implementing a robust, electronic delivery versus
payment system; establishing a guarantee fund to underpin the performance of transactions
and developing a rule book to reflect best international practice.
BESA‘s clearing operations were developed through using the Group of Thirty (G30)
recommendations on clearing and settlement as a blueprint. With the introduction in 1994 of
a recognised clearing house, the Universal Exchange Corporation Ltd (UNEXcor), BESA
11
members were able to benefit from electronic trading, matching and settlement. The SARB
was one of five settlement agents, along with four commercial banks. In November 1997,
BESA became the first exchange in Southern Africa to change to a t+3 rolling settlement,
eliminating the key risk, transaction risk, faced by market participants. BESA also utilised a
central depository where some 84% (by value) of securities were immobilised (Hove:
2008:40)
According to Hove (2008:40), by the late nineties, the secondary bond market had
developed to such an extent that the SARB decided to decrease its market making role in
this market. In 1998, the market-making role previously undertaken by the SARB was
transferred to a panel of 12 primary dealers, selected from both local and foreign banks to
improve efficiency and transparency in the secondary market. The selection was based on a
set of criteria to ensure that such primary dealers would have the capacity to deal with the
inherent risks associated with market making (AACB, 2006: 6). In 1998, in spite of the
improvements which were made in the bond market, the market was still exposed to a
number of risks. BESA‘s revenue was based on market turnover. The danger with that
income structure was the exposure to a decline in market turnover. Due to the Asian
financial crisis in that year, vast pools of money which was circulating around the world
settled in South Africa resulting in high turnover. Also, bond traders found themselves
operating in a highly unpredictable market, buying and selling frequently contributing to the
high turnover (BESA 2004). However, the local interest rates soared above 20% that year
and the yield on the government‘s benchmark R153 rose to above 15%. With the decline in
market turnover becoming inevitable, BESA had to look for other additional sources of
income (BESA 2004).
2.2.4. Recent developments
Recent developments on the bond market include the immobilisation and subsequent
dematerialisation of all bonds listed on BESA. With immobilisation, the securities are held in
a central securities depository (CSD) in paper or electronic form, to facilitate subsequent
book entry transfers (Hove: 2008:40). Also, BESA and the Actuarial Society of South Africa
implemented a series of indices for government and corporate bonds which included:
the Other Bond Index (OTHI) which reflects the performance of the 13 most liquid
bonds on BESA
the Government Bond Index (GOVI) which reflects the performance of seven
benchmark government bonds
12
the All Bond Index (ALBI) which is a combination of these two indices and comprises
of 20 different bonds selected for their size and liquidity.
With dematerialisation, securities will be issued (and traded) without a physical certificate
where ownership of a security exists only as an electronic accounting record. The reason for
doing away with paper based (or certificated) scrip issues is to facilitate easier payment and
transfer of ownership, which implies a reduction in cost and risk to all parties concerned,
namely issuers, brokers, bankers, transfer agents, clearing and depository agencies, and
thus ultimately, investors (Botha 2007:11).
South African financial institutions have also made a significant contribution to facilitating the
provision of capital and financial services on the continent. To enhance their potential in this
regard, the following reforms to exchange controls were announced; South African banks
were allowed to hold foreign assets of 25 per cent of their domestic regulatory capital as part
of the shift from exchange controls to the prudential regulation of banks‘ foreign exposures.
The limit was later on revised to 35 per cent; the foreign exposure limit on collective
investment schemes is increased from 20 per cent to 25 per cent of total retail assets, and
for investment managers from 15 per cent to 25 per cent of total retail assets.
Another important development in the domestic bond market was the listing of the first
inflation-linked bonds which are also called index bonds issued by the National Treasury in
2000. By 2006, there were four inflation-linked government bonds in issuance, with a total
market capitalization value of R60 billion which represented more than 11% of government
debt (AACB 2006:6). These instruments are helpful in providing information about inflation
expectations through readings of break-even inflation rate. Inflation-indexed SA government
bonds are issued at R1 million denominations. The principal amount is adjusted with
reference to any increase or decrease in the Consumer Price Index. As at the end of March
2011, there were four inflation-indexed bonds with a total cash value of R32 billion.
According to Futuregrowth (quoted in Faure 2006:40), ―such bonds are appropriate for
investors who wish to meet inflation-linked liabilities by paying an inflation-adjusted principal
at maturity as well as a fixed coupon of the adjusted principal‖. These bonds are attractive to
investors as their returns are not highly affected by inflation as they change as inflation
changes. However, it is argued that it is due to the buy-and-hold nature of the investor base
that results in the low turnover in these instruments.
The introduction of Separate Trading of Registered Interest and Principal (Strips)
programme of government securities in November 2001 by the National Treasury was
13
another important development. In this case a bond can be separated into its constituent
interest and principal payments and each of these cash flows traded as individual
instruments. Strips make it possible for investors to invest smaller amounts and to fine-tune
the duration of their portfolios (AACB 2006:6).
The introduction of retail bonds for 2, 3 and 5-year maturities by the National Treasury in
2004 was another important milestone in the further development of bonds in South Africa.
This resulted in smaller investors being able to access the bond market. Amounts ranging
between R1 000 and R1 million were allowed to be invested in any of the three maturities on
the fixed-rate and inflation-linked bonds. This programme has been a success from the
onset, and serves as an example that can be used by other developing and emerging-
market countries wishing to expand their domestic bond markets (AACB 2006:7).
A more recent development is the acquisition of BESA by the Johannesburg Stock
Exchange (JSE). BESA became a wholly-owned subsidiary of the Johannesburg Stock
Exchange (JSE) on 22 June 2009, the operative date of the scheme of arrangement in terms
of which the JSE acquired the entire issued share capital of BESA. The JSE‘s intention with
the merger was to harness the respective areas of expertise of the two exchanges to deliver
increased liquidity, increased functionality and a broader range of products and services to
market participants, bond issuers and investors, such as retirement funds, insurance
companies and their members (JSE 2010).
2.2.5. BESA market structure
BESA offered a secure and efficient dealing environment for the products for which it was
created. These are rand denominated debt securities mainly bonds as well as money market
securities (issued by government, public enterprises and the corporate sector) and
derivatives. However, its main product is central government bond (Faure 2006).
BESA is structured in a way to create a clear distinction between users (issuers and
members of the market associations), rights holders (affiliated to shareholders) and
stakeholders (the SARB, the regulators, the investment community and the Debt Issuer‘s
Association (DIA)). Jointly, stakeholders form part of the stakeholder forum, whose aim is to
make sure that BESA and the market associations fulfil their license requirements and that
the market functions effectively in terms of good market practice (BESA 2007). Figure: 1
shows the structure of BESA as of 2008.
14
Figure 2.1: BESA Market Structure
KEY:
BTA-Bond Trader‘s Association
MA- Market Association
Source: Hove (2008:43)
Market associations are groups of users that contract with BESA for a package of tailor
made services. This means that users of the exchange benefit from the ability to choose how
to trade and with whom, as well as how they wish to influence and develop the market (Hove
2008:43).
As in 2008, the market associations were divided into three main categories and were
governed by their own rules which are compliant and consistent with the core rules of BESA.
These categories are:
The Bond Traders Associations (BTA), formed by bond traders to represent the
welfare of the trading community in South Africa.
The Derivatives Traders Association (DTA): for the interests and views of firms
registered to trade BESA-listed derivative instruments.
The Debt Issuers Association (DIA): to guide and steer transformation within the
markets by dealing with both operational and strategic issues (Hove: 2008:43).
15
2.2.6. Size and performance of the bond market
The South African bond market is dominated by public debt issues, of which government
accounts for more than two thirds of all public sector issues. Government debt instruments
used to obtain funds consist of Treasury bills and government bonds. Treasury bills are used
by government usually to finance deficits and they mostly have maturities of 91, 182, 273
and 364 days and the interest rates on these instruments serves as a benchmark of other
money market instruments. The medium to long term debt instruments issued by the
National treasury includes fixed income bonds, inflation-linked bonds, floating-rate notes and
retail bonds. Among the different types, government bonds, treasury bills and Strips qualify
as Reserve Bank liquid assets for collateral.
The South African bond market‘s performance has been outstanding relative to other
emerging markets. Van Zyl (2009) shows that the South African bond market is a leader in
terms of the number of bonds listed and turnover. Table 2.1 below shows the sizes of the
foreign debt and domestic debt securities market in a few countries at the end of 2006.
Table 2.1: Size of the securities market at the end of 2006 (Billions US dollars)
Source: Authors computation based on data from Van Zyl et al (2009)
Government
Financial
Institutions Corporates Government
Financial
Institutions Corporates
Australia 10.6 371.9 16.9 97.1 215.4 144.8 1095.9
Denmark 258.6 2221.8 110.7 1222.7 881.8 143.2 1637.6
Germany 1.3 326.6 9 111.3 98.2 13.8 1212.4
Switzerland 6.4 1749.9 225.4 835.1 379.4 23.1 3794.3
United Kingdom 8 12.2 5.6 69.8 25.3 14.3 711.2
South Africa 44.9 28.9 19.8 169.1 112.5 27.4 345.3
Mexico 55.3 2.3 3.9 60.4 4.9 11.4 51.2
Argentina 3.7 22.4 6 59.2 33.9 53 235.6
Malaysia 33.8 6.1 0.4 129.5 148.8
South Korea 7.7 64.9 28.2 459.9 291.9 258.2 834.4
International Debt Securities
Country Equities
MarketIssuer Issuer
Domestic Debt Securities Market
16
Table 2.2: S.A Bond Markets Performance
Source: Authors computation based JSE: Market performance data (2012)
Table 2.1 shows that the size of the bond market in South Africa is relatively large, even
compared to some of the developed countries. As far as emerging economies are
concerned, only South Korea has a larger domestic government bond market than South
Africa at the end of 2006. However, the South African bond market has continued to grow
over the years as reflected in table 2.2. The market reached a record of more than R2.3
trillion in value of bonds traded at the end of May 2012 and out of that, 19.4 per cent was
contributed by foreign participants in the market.
The turnover ratio is defined as the nominal value of turnover in bonds divided by the
nominal value of outstanding debt stock (JSE 2010:8). Liquidity is an important aspect of
well-functioning markets as it provides investors with the ability to diversify risk. Even though
the measure of liquidity used in this instance is not completely reflective of overall liquidity,
as it does not account for transactions that occur over the counter in informal markets and
are therefore not recorded, this does not affect the South African bond market as it is an
exchange traded market not an OTC. The South African bond market is also one of the most
liquid emerging bond markets in the world. This is indicated in figure 2.2 below which
indicates turnover ratio (turnover over previous year‘s outstanding stock) of government
bond markets in several countries as of 2008. Based on 2008 data, the South African bond
market was also considered as one of the most liquid emerging bond market in the world.
This is indicated in figure 2.2 below which indicates turnover ratio of government bond
markets in several countries as of 2008.
Month & Year Dec-10 Jan-11 Dec-11 Jan-12 Feb-12 Mar-12 Apr-12 May-12Trading value - bonds
(Rmillion) 931,856 1,629,313 1,080,655 1,665,640 1,925,166 1,979,072 1,651,063 2,235,585
Trading volume - bonds 18,928 26,948 20,058 30,369 33,278 34,615 27,268 36,124
Average trade size -
bonds 49 60 54 55 58 57 61 62
Liquidity in the bond
market 0.82 1.43 0.95 1.47 1.69 1.74 1.45 1.97
Foreign involvement
(%) 36% 28% 19% 21% 21% 20% 20% 19.39%
Issued Amount (R
million) 1,135,945 1,135,945 1,135,945 1,135,945 1,135,945 1,135,945 1,135,945 1,135,945
New products listed:
bonds 54 38 43 123 91 118 73 91
17
Figure 2.2: Liquidity in the government bond markets
Source: Authors computation based on JSE Fixed Income Survey 2010
Figure 2.2 shows the total value of government bonds traded during 2008 divided by the
total value of government bonds outstanding at the end of 2008. It shows how many times
on average the outstanding bonds changed hands during 2008. Turnover ratio is an
indication of the liquidity of the bond market. A high turnover ratio indicates that an investor
should find it relatively easy to sell a bond in the secondary market. The overall turnover of
all bonds traded on the BESA in 2008 according to fixed income survey conducted by JSE
on behalf of WFE (2008), was 14.6 up from 13 in 2005. This is very high, compared to other
developed as well as emerging economies. The development of the South African bond
market was mirrored with developments in other markets (equity and futures) as indicated in
table 2.3 below. Table 2.3 shows that the bond market grew at an annual average of 14.1
per cent between 2001 and 2008. For the same period, GDP per capita appreciated at an
annual average of by 2.6 per cent. Also the underlying value of equities and futures
contracts experienced an average increase of 20.6 per cent and 41.2 per cent, respectively.
Adelagan (2009) suggest that the growth in the bond market and equity market have
contributed to the growth of the futures market in South Africa by facilitating the introduction
of a number of equity and bond market related instruments
0 2 4 6 8 10 12 14 16
National Stock Exchange
TSX Group
Shanghai SE
Borsa Italiana
Egyptian Exchange
Buenos Aires SE
Korea Exchange
Irish SE
Six Swiss Exhange
Colombo SE
Malta SE
Colombia SE
Mauritius SE
Istanbul SE
Oslo Bors
Tel Aviv SE
NASDAQ OMX Nordic
BME Spanish Exchange
London SE
JSE
Turnover Ratio
18
Table 2.3: Growth in the S.A Financial Markets
Source: Adelagan (2009)
The table above shows that the importance of a bond market goes beyond financing
government deficits but aiding the development of other financial markets. From December
2010 to May 2012, the average growth rate on the total value of bonds traded was 5.25 per
cent per month from R935 billion in December 2010 to R2.235 trillion in May 2012. Futures
grew by 5.04 per cent from R14 billion in December 2010 to R31 billion in May 2012, for the
same period options contract grew by 1.73 per cent from R138 million in December 2010 to
R182 million in May 2012for the same period.
Hove (2008: 44) highlighted that, ―at the beginning of 2006, BESA estimated that market
turnover for the year ahead would be of the order of R7.8 trillion, given the steady decline in
turnover year-on-year since 2002 and the bearish views of market commentators. However,
against all expectations, nominal turnover increased during 2006 by 41%, reaching R11.4
trillion, close to the turnover levels last seen in 2002. The annual turnover of bonds
registered on the Johannesburg Stock Exchange increased from R13.4 trillion in 2009 to
R16.9 trillion in 2010, and trades in RSA bonds abroad were R2.9 trillion, bringing total
trades in domestic bonds to R19.8 trillion. This is also reflected in figure 2.3 below. In terms
of instruments traded by sector, Figure 2.5 shows that government bonds remained the most
traded instrument over the years, contributing 93% to turnover. BESA attributed the increase
in turnover to volatility in the bond market created by various external events such as
increases in the Reserve Bank‘s repo rate, the depreciating rand, high bond yields and
increased holdings and trading by foreign investors (BESA, 2007c: 1).
2001 2006 2007 2008
Average
2001-2008
GDP/Capita percentage growth rate 0.78 4 3.8 1.9 2.6
Capital Markets: Percentage change
in the value of equity 31.7 21.2 47.9 -13.5 20.6
Capital Markets: Percentage change
in the value of bonds 70.7 40.8 18.4 29.5 14.1
Capital Markets: Percentage change
in future contacts 138.1 67.1 68.2 -53.7 41.2
19
Figure 2.3: Turnover on domestic and International bond exchanges (1995-2010)
Source: National Treasury 2011
On a month on month basis, in 2008 monthly turnover volumes rose from R1.6 trillion in April
to R2.1 trillion in September before falling off to R1.2 trillion in December. A number of
factors were mentioned for the higher volumes. These included monetary policy changes,
with the Reserve Bank tightening interest rates during the first half of the year; the collapse
of global equity markets and thus a flight to the relative safety of government bonds; and,
ultimately, the peak of the global credit crisis during the latter part of the year which resulted
in heightened risk aversion (BESA 2008). These factors continued to contribute to the
decline in the monthly turnover in the bond market as by December 2010, turnover declined
to R931 billion, picking up to R1.9 trillion in March 2011, R1.7 trillion in June 2011, R2.1
trillion in September 2011, R1.1 trillion in December 2011, R2 trillion in March 2012 and R2.2
trillion in May 2012 (JSE statistics). Repo transactions continued to constitute a substantial
portion (61 per cent) of the turnover recorded on BESA, with spot trades accounting for 35
per cent in 2008. Whilst these proportions were stable relative to 2007, in nominal terms,
repo transactions increased by 36 per cent year on year in 2008 as the demand for liquidity
in financial markets intensified. This was also consistent in 2009 as repo transactions
accounted for 67 per cent of total turnover. For 2008, repo transactions were particularly
higher in September and December, with the average size of a repo trade peaking at R110
20
million in September. The developments in the local repo market mirrored developments in
other major repo markets (the US and the Euro and UK repo markets) (BESA 2008).
Although the decline on month to month basis decreased to the later part of 2010, monetary
authorities were still cautious about the effects of the credit crisis.
According to the National Treasury budget review (chapter 6: 2011); the R157 (13.5 per
cent; 2014/15/16) bond remains government‘s most liquid debt instrument, with a turnover
ratio of 74 times its outstanding amount. The R206 (7.5 per cent; 2014) bond has replaced
the R186 (10.5 per cent; 2025/26/27) as the second most liquid fixed-income bond. Turnover
on inflation-linked bonds remains low due to the buy-and-hold nature of the investor base.
Figure 2.4 below highlights the performance of the yields of different government bonds. The
movements highlight that, over time, the yields on the different types of government bonds
moves together.
Figure 2.4: Government bond yields
Source: Author’s computation based on Strate data 2012
0
5
10
15
20
25
R157 R186R207 R208
21
Figure 2.5: BESA Markets Trade by Sector Q3/2011 (%)
Source: JSE Quarterly Review of Interest Rate Markets: September 2011
National Treasury highlighted that, it is due to the strength of South Africa‘s macroeconomic
indicators and higher global demand for emerging market debt that has led to rising
international interest in South African government bonds. Non-residents‘ purchases of
domestic bonds more than doubled from a net R27 billion in 2009 to a net R56 billion in
2010. In the first nine months of 2010, non-residents purchased a net of R73 billion worth of
domestic bonds, leading to a decline in bond yields. In the last quarter of 2010, yields rose
as investors shifted into equities (National Treasury: 2011:81) (also see figure 2.6, below).
22
Figure 2.6: Monthly foreign participation and R208 bond yield 2012
Source: Authors computation based on National Treasury data 2012
As it can be seen in Figure 2.7 below; the domestic pension funds own the largest share
(36.5 per cent) of government‘s bond portfolio, followed by non-resident investors (21.8 per
cent) as shown in Figure 2.6 above. The attractiveness of the South African debt market has
led the government to suggest that it will be able to manage the impact of a sudden
moderation in global capital flows should it occur.
6.6
6.8
7
7.2
7.4
7.6
7.8
8
8.2
8.4
(10,000.00)
-
10,000.00
20,000.00
30,000.00
40,000.00
50,000.00
60,000.00
1/2/2012 2/2/2012 3/2/2012 4/2/2012 5/2/2012 6/2/2012
M
i
l
l
i
o
n
s
Cumalative non-resident bond flows R208
23
Figure 2.7: Domestic Government bonds ownership 31 December 2010
Source: National Treasury 2011
The size and performance of a bond market can also be described in terms of market
breadth and depth. Mboweni (2006: 3&4) shows that market breadth is usually described by
the size of a market and number of participants. As of 2005, BESA had a total value of R700
billion listed bonds. The total value of bonds listed constituted about 46 per cent of gross
domestic product for 2005. As of October 2006, the market capitalisation of the JSE was
close to R4.7 trillion, more than three times the size of GDP for 2005. As at the end of 2006,
bonds of more than 70 different issuers were listed on BESA, representing the central
government, local government, parastatals and the private corporate sector. By the end of
March 2012, the total nominal value of bonds traded on JSE was R5.6 billion for 2012. Total
market capitalisation on bonds at the end of 2010 was R170 billion US dollars (WFE)
According to Mboweni (2006:4) market depth refers to liquidity and is defined as ―market
liquidity which is the ability to execute transactions of a representative size cheaply and
rapidly without having too much of an effect on price‖. There are a number of proxy
measures of liquidity which include the bid-ask spread, turnover per year and the turnover as
a ratio of market capitalisation. Figure 2.6 shows depth as a ratio of GDP. As indicated in
Figure 2.8, market depth reached its highest level in 2000. However, from 2001 to 2007
there have been marginal increases of less than 5 per cent to 10 per cent. In 2008, the
South African Bond market witnessed a further decline in market depth from 2007 (see
Figure 2.8). Market depth of the bond market measured by the ratio of the nominal value of
bonds issued at the end of the year to GDP, was sacrificed as issuance conditions tightened
24
while GDP growth persisted, mostly during the first half of 2008. The decrease was
attributed to both government and corporate bonds unlike in 2007 where contraction was
entirely on the back of lower government debt issuance (BESA 2008).
Figure 2.8: Market Depth (2000-2008)
Source: BESA 2008
Market breadth on the other hand describes both the size of the market as well as the
number of participants as discussed below. The turmoil in global financial markets as well as
conditions which were prevalent in the domestic economy in 2008 rendered the expansion of
the South African primary bond market difficult. However, bond listings grew by 5.6 per cent
in 2007 which has been described as the slowest rate of growth since 2002. The bulk of this
growth was attributed to the increased issuance of commercial paper by corporates as well
as increased issuance by state owned enterprises which comprised 25 per cent of the
increase. Banks and the government were the other key contributors, which contributed 21
per cent and 20 per cent respectively (BESA: 2008).
Mboweni (2006: 5) highlighted that there continues to be strong demand for bonds from non-
residents for South African financial assets. Non-residents make a significant contribution to
turnover and liquidity on BESA. In 2006, non-residents purchased bonds totalling R26.8
billion and increased trading activity from 32% in 2005 to 38% in 2006. However, out of the
total value of R2.235 trillion traded at the end of May 2012, non-residents purchases
amounted to 19.4 per cent or R425 billion. There seem to be a continued increase in the
development of the South African bond markets. As highlighted by Mboweni (2006: 50)
25
―....unlike many African and emerging-market countries, South Africa relies more on its
domestic bond market than on international borrowing. This is partly due to historical
reasons, but also due to the preferences of the current government and the sound fiscal and
monetary policies‖.
Figure 2.9: Securitisation (%) of growth listing 2008
Source: Authors computation based on BESA data (2008)
For the year 2008, ―growth in listings was dampened by contractions in the securitisation and
other corporate categories as redemption far outweighed issuance‖ (BESA 2008:2). As
indicated in Figure 2.9, general risk aversion due to the global financial crisis rendered
securitisations less attractive in 2008. Also, the squeeze on the consumer caused by high
interest rates due to monetary policy tightening also contributed to the slump in the local
housing and vehicle markets, thus slowing credit extension by financial institutions resulting
in a decrease in the amount of bonds issued in the market. As the effect of the global
financial crisis continued in 2009, securitisations issuance continued to decline in 2009 by
R21.6 billion or 19.3 per cent relative to December 2008. This was compounded by both a
lack of demand and supply.
20
10
25
1
21
-30
-7
18
37
5
-40
-30
-20
-10
0
10
20
30
40
50
Centralgovernment
Municipal SOEs Waterauthorities
Banks Securitisations Othercorporates
Credit linkednotes
Commercialpaper
Dual listing
Securitisation (%) of growth listing
26
2.2.7. Listing requirements
Faure (2009: 94) shows that the primary role of the financial exchange (BESA) is to provide
a stable and safe environment for the trading, clearing and settlement of debt securities
issued by the central and local government, parastatals and the corporate sector business in
a transparent, efficient and orderly market place, contributing to capital formation in the
economy. According to Faure, this role should also be seen in an even wider context as a
contribution to financial stability, which broadly can be described as having two legs: price
stability and stability of the group of institutions that make up the financial system. The
financial exchanges are part of this group. BESA achieves this by implementing listing
requirements which are based on international best practice. This includes:
A flexible and not so cumbersome process;
An environment that supports full disclosure and investor confidence;
Documentation that is comprehensive; and
Requirements that are not onerous but with set standards and also cost effective
(Jones 2007).
BESA‘s listing requirements are aimed at:
Specifying the rules and procedures governing new applications as well as
continuing responsibilities of issuers.
Dictating minimum disclosure requirements.
The main functions of the listing requirements mentioned above are to provide investors with
confidence and the ability to make an informed assessment of the nature and state of an
issuer‘s business. These requirements are an instrument to uphold suitable disclosure to the
market (BESA 2009). The bonds traded on the exchange are contained in a ―list‖ of
securities that BESA maintains, hence the term ―listed securities‖, and the issuers are
obliged to comply with the listing requirements of BESA. These requirements include the
issue of a placing document (prospectus), strict financial disclosure requirements (Faure
2009:86). Scrutinisation and approval of all listing applications, supporting documentation
and matters relating to listing disclosure requirements is performed by the listing committee
appointed by the executive committee of BESA. Also, in terms of the rules and listing
requirements, the listing committee has the power to consolidate, suspend, remove or
modify from time to time a listing of a debt security as well as requirement for the listing of
debt securities (BESA 2007).
27
2.2.8. Trading, Clearing and Settlements
The South African bond market is largely a wholesale market, with less frequent trades
relative to the equity market, but it is far bigger in value and transactions takes place
between a limited numbers of major players. In the secondary market, interdealer brokers
(IDBs) have been key players in the development of governments and corporate bond
market. In terms of trading, the IDBs are a sub category of trading member of the JSE and
trades solely on order-driven basis.
Previously, the Bond exchange of South Africa (BESA) was operating a bond capture
system, BTB, which allowed members to match trades entered and then routed the matched
deals through to Strate. However, with the subsequent merger with the JSE, two operating
systems were operating at JSE, namely the Yield-X and BTB. Initially, the JSE proposed that
that all Report only trades be reported on the JSE on the BTB system, and all Central Order
Book bond trades are to continue trade and execution on the Yield-X system.
There are four types of trading systems that are used in the bond market; namely
Floor trading
Telephone-screen trading
Screen-telephone trading
Automated trading [on an automated trading system (ATS)]
(Faure: 2009:91)
Until the late nineties, the bond market made use of the floor trading method (also called
open-outcry trading) until October 1998 according to Faure (2009), deal execution now takes
place via two trading ―systems‖ in South Africa:
Telephone-screen trading: in this system, market makers place indication rates on
information vendor (IV) screens like the Reuters Monitor Service and deals are
negotiated and consummated on the telephone. Some market makers do not
advertise prices on screen and only quote prices to clients on the telephone. As
noted, this is a quote-driven market where the market makers quote buying and
selling rates.
Screen-telephone trading: in this system, the interdealer brokers quote firm rates on
IV screens, and the telephone is used by members of the exchange to ―take‖ (buy) or
―give‖ (sell), such as confirming the transaction with the interdealer broker. They also
28
advertise prices via their squawk boxes. As indicated, the interdealer brokers only
deal with the BESA members and not with the investors.
When bonds are initially issued, they are lodged with the Central Security Depository (CDS).
A CDS maintains and provide the infrastructure for holding uncertified securities and a
settlement. Strate Limited is the licensed CDS in South Africa and operates the settlements
system to facilitate electronic settlement for the bond trades. All members of the Exchange
must appoint a settlement agent, also referred to as CSDP. These CSDP includes the
ABSA, FNB, NEDBANK STD BANK and the SA Reserve Bank.
For settlement purposes, bond deals through BESA are conducted on a netted, T+3 rolling
settlement systems. The institutions involved in clearing and settlement are; a clearing
house (STRATE), a central securities depository (STRATE) and a settlement agent system.
The Exchange offers protection from settlement failure and tainted scrip risk through its
Guarantee Fund, and members‘ compulsory fidelity cover provides protection against fraud
/theft perpetrated by employees of a member firm. In a drive to align South African
settlement practices with international best practice, the Exchange adopted the G30
recommendations on clearing and settlement systems, in November 1997 the Exchange
introduced T+3 continuous, rolling settlements (Strate 2012). There is a standard trade
which means a trade that is to be settled on the third business day after trade date. This is
also referred to as a ―Spot‖ trade. There also exist a non-standard trade which means a
trade which is to be settled less than three business days after trade date. Same day
settlement (T+0) is permitted in the bonds environment and a forward-dated trade means a
trade that is to be settled at a future date more than three business days after trade date.
The procedures for clearing and settlement are contained in a detailed BESA document
entitled ―Electronic nett settlement process in the South African bond market‖ (Faure:
2009:90). The JSE regulates all on-market transaction and these transactions are reported
to the Nutron System and settle on a net basis. Subsequent to BESA merger with the JSE,
BESA in 2008 had partnered with NASDAQ OMX Group to establish BondClear, a clearing
solution that includes matching, central counterparty (CCP) services, risk management and
settlement. BondClear could not perform the functions of a CCP itself, but it would be
undertaken by OMX Nordic Exchange Stockholm AB, which is part of the NASDAQ OMX
group.
29
2.3. Overview of the South African Equity Market and the JSE
Equity or stock market is the institutional framework through which public or private
companies issue new share capital in the primary market and the ownership of the shares
changes hands in the secondary market (Goodspeed 2007:10). The role that a stock market
plays in spurring industries of a country as well as economic growth is enormous. New
shares are issued in the primary markets whilst the secondary markets exist for the trading
of already existing shares. As in Bernanke (2003), stock prices are among the most closely
watched assets in the economy, this is due to the perception that they are good in predicting
business cycle and they have been used to do exactly that in the past (Muroyiwa 2011:9). It
is argued that asset prices are leading indicators for future changes in economic activity
because they reflect the discounted value of expected future dividends, and thus expected
future growth of the economy.
JSE is licensed in terms of the Securities Services Act (SSA) of 2005 (Act No.35) in which
Section 8 of the Act also allows the existence of more than one stock exchange though there
is still only one stock exchange in South Africa. The JSE including the former Bond
Exchange of South Africa is a licensed exchange in terms of SSA. JSE is regarded as
mature, efficient and a secure market with a world class regulation, trading and settlement,
assurance and credit risk. It is also argued that excessive regulation in financial markets may
discourage prospective investors, but this does not mean there should be no regulation as
investors are confident with a stock exchange where a proper regulatory framework is in
place and regulatory authorities rigorously enforce it as well as make sure that market
participants adhere to it. The South African stock market is regulated only to the extent of
protecting members of the general public in buying and selling of shares without unduly
infringing upon self-regulation. The legislation that governs the JSE is embodied in the
Securities Services Act, Act 36 of 2004. Transactions in the primary market affect the size of
the equity pool as opposed to transactions in the secondary market which do not affect the
number of shares in issue (Equities Trading Manual, 2008). The secondary market is a
market of the sale of previously owned securities, prices are determined by demand and
supply in the market. The trading system for the secondary market in South Africa is through
an electronic system called JSE Trade Elect (Equities Trading Manual, 2008).
The critical role that the secondary market plays is based on the major influence it has on
the primary market. The primary market only functions successfully as a result of the liquidity
that the secondary market provides. Investors want the guarantee that they can easily
convert equity into cash at known prices; at any time since they don’t want to be stuck with
30
meaningless paper hence the invaluable role of the secondary market. Stock markets
around the world operate under two trading systems, the order driven system and the quote
driven system. They may use both or either of these two trading systems. The JSE uses the
order driven pricing system, buyers and sellers submit bid and ask prices of a particular
share to a central location where the orders are matched by a broker while in the quote
driven market individual dealers act as market makers by buying and selling shares for
themselves (Godspeed, 2008; 11). The market price of equity can change throughout the
day and selling prices are quoted continuously (Equities Trading Manual, 2008; 14)
(Muroyiwa 2010:45).
Centred on the Johannesburg Stock Exchange, the South African Equity markets has
undergone major changes over the years in terms of legislation reforms, access to the
markets, foreign participation as well as trading methods. The JSE was established in 1887
and it is the largest stock exchange in Africa i.e. in terms of market capitalisation, volumes
traded, market participants and regulations. The JSE Equity Market is segregated into the
Main Board, the Alternative Exchange Board (AltX) and the Africa Board. This provides
companies and investors with a myriad of listing and investment opportunities which are well
suited to cater for their specific needs. Through the JSE Equity Market investors are also
able to trade a variety of products including Warrants, Exchange Traded Products such as
Exchange Traded Funds and Exchange Traded Notes and other Investment Products.
2.3.1. Early development and structural improvements of the equity markets
The discoveries of gold in 1886 in the Witwatersrand sew numerous financial institutions and
new mines launch as ‗Gold Fever‘ gripped the country (JSE: 2012). Due to the discovery of
gold and the manner in which the economy of the country was developing, it quickly became
apparent that a stock exchange was needed to facilitate funding for the booming South
African mining and financial industry. Thus, in 1887 the Johannesburg Stock Exchange
(JSE) was born. In June 2006, over 100 years later, the JSE itself became a public listed
company on the JSE.
In a bid to improve the stock market, in 1947 a first legislation applicable to the operation of
exchanges was introduced with the Stock Exchanges Control Act. In 1963 JSE become a
member of the World Federation of Exchanges, this further exposed the companies listed on
the JSE to the world as all information became available in the WFE data base. The JSE
sew improvement on its trading activities as in 1978 the JSE achieved a market
capitalisation of R51 billion, eight times the market size in 1961 and a record for the JSE.
31
Another development came in by the introduction of the 1979 Kruger Rands which were
officially listed during that year.
In the 1990‘s, the most important economic event was the unification of the dual exchange
rate on March 13, 1995. This was accompanied by the relaxation of exchange controls on
residents and the removal of exchange controls on non-residents (Beelders 2002:2). During
1995, substantial amendments were made to the legislation applicable to stock exchanges
which result in the deregulation of the JSE through the introduction of limited liability
corporate and foreign membership. The South African Institute of Stockbrokers was also
formed to represent, train and set standards for the qualification of stockbrokers (JSE 2012).
In December that year, the market capitalisation exceeds R1 trillion for the first time which
highlighted that the amendments of the legislation and other reforms improved participation
in the market.
The abolishment of the exchange controls on non-residents was due to the abolishment of
the financial rand. The local sales proceeds of non-residents owned South African assets
were regarded as freely transferrable from the Republic. In accordance with the principle of
relaxing exchange controls, permission was granted in June 1995 to South African
institutional investors (Long-term insurers, pension funds and Unit trusts) to exchange
through approved asset swap transactions part of their South African portfolio for foreign
securities. At first the limit to enter into asset swaps by institutional investors was 5 per cent
of total assets and in June 1996, the limit was raised to 10 per cent of total assets.
Institutional investors were permitted to transfer abroad 3 per cent of their net inflow of funds
generated during the 1995 calendar year within the overall limit of 10 per cent of the total
assets.
In 1996 the open outcry trading floor was closed on 7 June and replaced by an order driven,
centralised, automated trading system known as the Johannesburg Equities Trading (JET)
system. Dual trading capacity and negotiated brokerage was also introduced. The value of
shares traded annually reaches a new record of R117.4 billion and the new capital raised
during the year reaches R28.4 billion.
Within the broader macroeconomic and political changes in South Africa, there were also
two positive changes on the JSE, first, brokerage fees on the JSE changed from fixed to
flexible in November 1995 after the introduction of the new Stock Exchange Control Act.
Second, the Johannesburg electronic trading (JET) system was phased in on the JSE from
March to June of 1996. In addition, there has also been a positive change on the South
32
African futures exchange. In 1995, the JSE indices underlying the futures contracts were
redesigned to consist of a smaller basket of stocks. The All Share Index that consisted of
over 400 stocks was replaced by an index consisting of 40 stocks, the ALSI40. Similarly, the
gold and industrial indexes were replaced by the GLDI10 and INDI25 indexes, respectively,
where the number at the end of each index denotes the number of stocks in the index. Each
of these changes reduced the cost of trading and hedging, and made the markets more
accessible to foreign investors (Beelders 2002:3).
In 1997 SENS (Securities Exchange News Service – known then as Stock Exchange News
Service), a real time news service for the dissemination of company announcements and
price sensitive information was introduced. The advantages of SENS were that it ensured
the early and wide dissemination of all information that may have an effect on the prices of
securities that trade on the JSE (JSE 2012). During 1999 the new Insider Trading Act was
introduced based on recommendations made by the King Task Group on Corporate
Governance, which included representatives from the JSE. The JSE also established, in
collaboration with South Africa‘s four largest commercial banks, the electronic settlement
system, STRATE and the process to dematerialise and electronically settle securities listed
on the JSE on a rolling, contractual and guaranteed basis was initiated.
In a drive to further develop the South African financial markets at large, in the Mid-Term
Budget Policy Statement (―MTBPS‖), the Government announced its commitment to the
gradual removal of exchange controls due to their negative impact on foreign investment and
domestic investor confidence, in the Budget Policy on 12 March 1997, the Minister of finance
announced that it was time to make significant changes to the exchange controls. South
African individuals and corporations were to be allowed the freedom to transact
internationally, as envisaged in the macroeconomic strategy, individuals were allowed to
remit R200 000 capital for investment abroad in any manner and in fixed property in SADC
countries; individuals were permitted to retain foreign income earnings in foreign currency
accounts; South African corporations were allowed to raise foreign funding on the strength of
their South African balance sheets and institutional investors were allowed to invest up to 3
per cent of the net inflow of funds, etc.
Since 1994 South Africa had progressively lifted restrictions on foreign exchange
transactions, thereby contributing to the openness and competitiveness of the capital
market. There were capital markets reforms that happened in 1998 which were aimed to
strengthen South Africa‘s commitment to the Southern African Development Community
(SADC) region through measures designed to facilitate regional capital market integration;
33
and to increase the limits on the activities of individuals, corporations and financial
institutions. These reforms includes amongst others; the limit for new investments into SADC
which was increased from R50 million to R250 million while the offshore investment limit was
increased from R30 million to R50 million and individual foreign capital allowance was
increased from R200 000 to R400 000, with a requirement for a clearance certificate from
SARS prior to approval of the investment (National Treasury 2012).
During 2000 the JSE successfully listed Satrix 40, the JSE‘s first exchange traded fund,
which tracks the top 40 companies listed on the JSE‘s Main Board. This is argued to have
provided a simplified and accessible mechanism for investors in gaining access to diversified
equity portfolio. Another form of development was achieved in 2001 when the JSE acquired
SAFEX, the South African Futures Exchange, and became the leader in both equities and
agricultural derivatives trading in the South African market (JSE 2012). The JSE entered into
a joint venture with GL Trade SA to provide an internationally accepted trading front-end to
the equities market, known in South Africa as TALX during the same year. Marais (2008:8)
highlighted that the addition of the financial derivatives market resulted in an increased
trading volumes on the underlying equities and this provided investors with the ability to gain
exposure to both the downward and upwards movement on the equity prices.
In 2002, all listed securities were successfully dematerialised and migrated to the STRATE
electronic settlement environment, with rolling contractual and guaranteed settlement for
equities taking place five days after trade (T+5). Since the completion of this process, the
JSE has had a zero failed trade record, thereby improving market integrity immeasurably
and representing a major milestone in winning both local and international investor
confidence (JSE 2012). The JET system was also replaced by the LSE‘s SETS system,
hosted by the LSE in London. The system, operated from London by the LSE, is called ―JSE
SETS‖. The JSE also introduces the LSE‘s LMIL system, known in South Africa as InfoWiz
and it provides a world-class information dissemination system and substantially improves
the distribution of real-time equities market information. More than just a change in
technology platforms, the introduction of JSE SETS also represented the forging of a
strategic alliance with the LSE and improved the international visibility of the JSE.
The JSE also took an important step forward in its campaign to modernise its operations with
the launch of a new free float indexing system in conjunction with FTSE, the FTSE/JSE
African Index Series to replace the then existing indices in 2002. The FTSE/JSE African
Index Series had enhanced the investibility of South African stocks by providing foreign
investors with an indexing system with which they are familiar, aligned to global standards
34
and which were easier to comprehend. Two new exchange traded funds were also
launched, which were the Satrix Fini, which tracks the top 15 financial counters and Satrix
Indi which tracks the top 25 industrial counters on the Main Board of the JSE. In 2003, the
JSE launched AltX which had been developed in partnership with the DTI. In 2004 the JSE
launched the Socially Responsible Investment (SRI) Index, which measured compliance by
companies with triple bottom line criteria around economic, environmental and social
sustainability.
The gradual relaxation of the exchange controls in line with progress in achieving relevant
preconditions such as macroeconomic stability, strengthening of the balance of payments
and financial sector development facilitated the steady reintegration of South Africa with the
global economy, while guarding against the macroeconomic risks of disruptive capital flows.
In a bid to enhance SA competitiveness in the global economy government further relaxed,
exchange control limits on new outward foreign direct investments by South African
corporates were abolished and only an application to the South African Reserve Bank
(―SARB‖) would still be required for monitoring purposes; South African corporates were
allowed to retain foreign dividends offshore and foreign dividends repatriated to South Africa
after 26 October 2004 were allowed to be transferred offshore again at any time for any
purpose. This allowed companies doing business abroad to have easier access for their fund
when a need arises for any purposes. These further strengthen the SA financial markets.
South African private individuals were also allowed to invest, without restriction, in inward
listed instruments on South African exchanges and while the foreign direct investment
allowances of R2 billion and R1 billion per project for foreign investment by SA corporates
remained in place, the percentage of the excess cost that can be funded from South Africa
was increased from 10 per cent to 20 per cent, with effect from 18 February 2004 (National
Treasury 2012).
With effect from 18 February 2004, foreign companies or foreign wholly owned subsidiaries
were permitted to borrow locally up to 300 per cent of the total shareholders' investment and
foreign firms were allowed to list on South African capital markets, thus allowing them to
raise debt and equity finance on the JSE Securities Exchange (JSE) and Bond Exchange of
South Africa (BESA). During 2004, institutional investors were allowed to invest 5 per cent
of their total retail assets in African securities listed on the JSE or BESA.
In 2005 the JSE launched Yield-X, its market for a wide range of interest rate products. This
allows for the trading of both spot and derivative interest rate products on one platform with
35
multi-lateral netting across all products (JSE 2012). The JSE demutualised and incorporated
in South Africa as JSE Limited, a public unlisted company on 1 July 2005. Existing rights
holders of the JSE become its first shareholders and for the first time in the JSE‘s history, a
person who is not an Authorised User of the JSE or a stockbroker can obtain an ownership
interest in the JSE (2012). Immediately on demutualisation, JSE rights were converted into
JSE Shares and each rights holder received 1 000 JSE Shares for every 1 JSE right held.
This resulted in the JSE having an authorised share capital of R40 million made up of 40 000
000 ordinary shares of R1.00 each, of which 8 340 250 ordinary shares were issued to
previous rights holders. Over the counter trading in JSE Shares commenced with settlement
of the trades occurring through STRATE. In 2006 June 2006, the JSE Ltd lists on the Main
Board
2.3.2. Recent developments
Government‘s gradual process of exchange control relaxation enabled an orderly process of
global reintegration, encouraging South African companies to expand from a domestic base
and allowing South African residents to diversify their portfolios through domestic channels.
Further steps in this regard included the announcement in 2007 that South African
companies involved in international trade were permitted to operate a single customer
foreign currency (CFC) account for both trade and services, and can use it for a wider variety
of permissible transactions; to deepen South Africa‘s financial markets and increase liquidity
in the local foreign exchange market, the JSE was granted permission to establish a rand
currency futures market.
To further enable South African companies, trusts, partnerships and banks to manage their
foreign exposure, in 2008, they were permitted to participate without restriction in the rand
futures market on the JSE Securities Exchange. This dispensation is also extended to
investment in inward-listed (foreign) instruments on the JSE and Bond Exchange of South
Africa.
2.3.3. Size and the performance of the South African Equity market
There are currently over 800 securities listed on the JSE Equity Market issued by over 400
companies which comprises of approximately 480 Equities, over 350 Warrants and more
than 20 Exchange Traded Funds. Approximately one fifth of the listed companies constitute
dual listings and about half of these companies have primary listings on other stock
exchanges throughout the world. Based on the world federation of exchanges, JSE was
ranked number 20 in the world in terms of market capitalisation at the end of 2011.
36
Stock markets around the world are no longer existing in isolation as the world economy has
become a global economic village in which different markets interact with one another and
economic activities in one market has a some influence in another market in a different
location. According to Beelders, the South Africa’s financial markets used to be heavily
regulated with controls and dual exchange rate regime and stringent exchange controls, all
these were originally introduced to stifle severe capital flight as a result of huge political risk
which was associated with investing in South Africa at the time (Beelders 2002:2). These
was all eased in the mid 90‘s and the political environment was stabilised by the release of
Nelson Mandela in 1990 as well as the holding of democratic elections in 1994 (Beelders,
2002; 2). The opening of financial markets which followed political developments in the
country, as well as the positive outlook the international community had for South African
economy, improved share prices. The advent of democracy in South Africa marked the lifting
of sanctions by international community, fiscal adjustment policies, moderate growth rates
and an introduction of a fluctuating exchange rate (Bosker and Krugell 2008). The unification
of the dual exchange rate in March 1995 was the most important economic event in the
1990’s (Beelders, 2002:2). This brought to an end the dual exchange rate on residents and
stringent exchange rate controls on non-residents. This went a long way in influencing
activity on South African stock market
The JSE Securities Exchange has three main indices listed as follows, the Resources Index,
Financials Index and Industrial index (JSE, 2010). Movements in the volume and share
traded are tracked as well as measured by the JSE all-share index which in actual fact is a
barometer of performance of listed companies. Beelders highlighted that, for the major part
of the last century, the South African economy has been resource based and this is also
evident in the South African stock market were precious metals such as gold has played a
major role. Consequently movements of the main index are driven by movements in the
Resources index which are a result of movements in resource prices especially gold and
platinum (Muroyiwa 2010: 45).
JSE All Share Index has generally been on an upward trend in the entire period of analysis
with a few definitive acute and deep declines and this is reflected in figure 2.10. Figure 2.10
serves to show that the market index has registered phenomenal growth over the years. As
reflected on the figure below, the all share index is monitored from January 2002 to May
2012. After the release of Nelson Mandela and uplifting of sanction that were imposed to SA,
JSE All share index performed remarkably well between 1991 and 1996. This was caused
by the relaxation of hostile controls and regulations and this made South Africa‘s stock
market more accessible and favourable to international investors with non-residents taking a
37
more active role in the bond and stock markets. It is also argued that the South African stock
market is largely resource based since the biggest listed companies are mining
conglomerates (Muroyiwa 2010:45). This means that to a large extent, movements of the
main index are driven by movements in the Resources index which are a result of
movements in resource prices especially gold and platinum. However, share in the different
sectors turn to be influenced by different factors. Figure 2.11 shows the earnings yield of
shares from different sectors.
Figure 2.10: JSE All Share Index Performance (2002-2012)
Source: Author’s computation based on data obtained from JSE (2012)
Evidence from figure 2.10 above, the South African financial markets have likewise been
heavily affected by the international turmoil, losing nearly half its market cap value over
2008. However, by January 2011, the JSE All Share Index had almost recovered to its pre-
crisis end of day high of 33233 (reached on 22 May 2008) and has traded in a relatively
narrow band since July 2010. It is argued that the regulatory framework, supported by the
Financial Services Board (the FSB), the JSE and Strate (as Self Regulatory Organisations -
SROs), protected against market disruptions over a time when other countries were suffering
settlement failures brought about by the bankruptcy of entities like the investment bank
Lehman Brothers and the insurer AIG (Money Markets Bill 2011:5).
0
5000
10000
15000
20000
25000
30000
35000
40000
JSE ALL SHARE
38
Figure 2.11: Earnings yield on JSE shares
Source: Authors computation based on JSE data 2012
Figure 2.12: Dividend yield
Source: Author’s computation based on JSE data 2012
0
2
4
6
8
10
12
14
0
5
10
15
20
25
30
35
40
01-J
an-1
996
01-A
ug
-199
6
01-M
ar-
19
97
01-O
ct-
199
7
01-M
ay-1
99
8
01-D
ec-1
99
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Industrial Financial
Resources All Share
0
1
2
3
4
5
6
0
2
4
6
8
10
12
14
16
Jan
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6
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9
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0
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0
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Oct-
20
11
Industrial Financial
Resources All Share
39
From figure 2.11 above, the yield of shares in the financial sector is slight higher than the
shares in resource sector and followed lastly by the industrial sector. This steady and stable
performance of the share market in the 1990‘s was hampered by the South –East Asian
economies financial crisis of 1997 (SARB, 1997).The JSE all-share index declined sharply in
October 1997 but it immediately recovered (SARB 1997). The worst was not over as the
economic events during the turmoil that had been caused by the Asian financial crisis
resulted in further instability between during 1997 and 1998; the global economy was also
affected. Because capital flows were been redirected to advanced economies, this resulted
in external finance constraints for emerging market economies and it deprived these
countries of the much needed capital inflow. As the South-East Asian economies were on a
recovery path towards the end of 1999, the stability in the financial markets was brought
back and there were signs of improvements in the world economic outlook. Figure 2.12
shows the dividend yield per share in the different sectors and the movements are similar to
the one in figure 2.11. The intuitions behind the movements in figure 2.12 are similar to the
one explained in figure 2.11.
At the time the world economy recovered from the effects of the Asian financial crisis, stock
markets around the world gathered moment yet again as a result of a boom in share prices
of companies who produce communications, information and computing technology
equipment (SARB 2002). As a result of the assets bubbles during the year 2000, a decline in
the JSE all-share index was experienced but that decline was transitory as the stock market
recovered quickly. Due to the fact that most countries in the world depended on the US
economy, September 11 terror attacks of 2001 in the US affected the economic activities of
that country as well as other major trading partners. The impact of the disturbances in the
US economy as a result of the attacks was more evident in the second quarter of 2002 in
South African stock markets as the JSE all-share index started to decline (See figure 2.10).
A major cause of this slowdown was the sudden slump in the demand for communications,
information and computing technology products and the accompanying fall in share prices,
which caused a sizeable loss of financial wealth (SARB 2002:1). Furthermore, the earlier
tightening of monetary policy to curb inflationary pressures and the rising oil prices also
weighed on economic activity and this effect translated into a worldwide loss of confidence
which caused delays in decisions on expenditure and investment and aggregate demand
was depressed not only in the United States, but also in the rest of the world.
Global economic activity picked up in the second quarter of 2003 and remained solid
between 2003 and 2006. However; the world was to be hit by a deeper economic crisis in
2007 and economic commentators compared argued that a deeper crisis like that was last
40
seen during the great depression of 1929 (Muroyiwa 2010: 46). By the end of October 2008
US$ 25 trillion had been wiped off the value of world stock markets (Naude 2009:1). This
argued partly to have been precedented by the seven-year period of high growth in equity
markets which also originated in the USA; consequently, it was anticipated by many that the
global slowdown should start in the emerging markets. Between November 2007 and
February of 2009 the all share index lost 41% of its value. Stocks markets all over the world
tumbled due to the dire effects of the global financial crisis of 2007/08 (SARB, 2007, 2008).
Figure 2.10 shows the biggest decline in the all share index since the slight decline that was
experienced in 1998 during the Asian financial turmoil as well as the effects which were
caused by the assets bubbles in 2000 to 2003. As reflected in figure 2.10, the index showed
signs of recovery from the third quarter of 2009 onwards as the index picked up and at
around the same time most major world economies which had been devastated by the
financial crisis where also showing signs of recovery (SARB, 2009). This continued until a
marginal decline was experienced in 2011 but again until May 2012, the index was showing
positive signs.
Another closely watched market indicator is how the market is capitalised. According to the
World Federation of Exchanges (2010), Stock Market (domestic) is defined as the total
number of issued shares of domestic companies, including all the different classes,
multiplied by their respective prices at a given time. Domestic market capitalization as shown
by the graph below (Figure 2.13) has behaved similar to the JSE all-share index. The peaks
and troughs were also largely influenced by the factors that influenced the movements on
the all-share index as discussed above. Market capitalisation as a stock market indicator is
mainly used by investors to monitoring whether This stock market indicator is usually used
for comparison purposes and to judge whether the value of listed companies in the local
stock market is increasing or not. This ensures that investors are well informed about the
performance of the companies listed in that particular stock exchange before any investment
can be made. The importance of market capitalization is that it is easier for investors to trade
in large capitalised company stocks, market capitalization also measures liquidity and
investors can make comparisons of stocks to trade in. On the international level investors
are most likely to trade in highly capitalised stock markets as they are more likely to have a
high level of liquidity which makes it easy for investors to dispose of the shares anytime they
need to liquidate their investments (Muroyiwa 2010).
41
Figure 2.13: Market Capitalisation (R’ Billion: 1975-2011)
Source: Author’s Computation based on JSE data (2012)
Another good indicator for stock market is the trading volumes. Trading volume is the total is
the total number of shares traded multiplied by their respective matching prices. It highlights
the sum of the total number of share traded at a specific priced. Mubarik and Javid (2009)
describe trading volume as a critical piece of information in the stock market because it
either activates or deactivates the price movements. It shows liquidity of the stock market
and can be used for comparisons of the performance of the stock markets. It can be argued
that an increase in price induce investors to trade more, thereby increasing trading. Trading
volume and stock returns are related due to their joint dependence on the rate of information
flow called the underlying common mixing variable (Nowbutsing and Naregadu, 2009).
Figure 2.14 below show JSE stock market trading volume.
0
1 000
2 000
3 000
4 000
5 000
6 000
7 000
8 000
R B
illio
n
Market Capitalisation
42
Figure 2.14 JSE Equities Volumes traded 2000-2011 (R’ Billion)
Source: Author’s Computation based on JSE data (2012)
Listed securities on the JSE are also argued to play a significant role in terms of capital
allocation (Marais 2007:1). The South African equity market has undergone a lot legislative
reforms and development over the years. This can also be seen on the amount that is
contributed by foreign participants. Figure 2.14 show how foreign investors have performed
over the years.
0
10
20
30
40
50
60
70
80
90
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
R B
illio
n
Volume traded
43
Figure 2.15 JSE equity markets foreign participation
Source: Author’s computation based on JSE data 2012
Another closely watched stock market indicator is the stock prices and stocks are also one of
the closely watched assets in the economy and this as highlighted earlier on is based on the
premise that past stock markets have been used to predict the business cycle. According to
Muroyiwa (2011:10), Fischer and Merton (1984) also ―acknowledged that economic theory
states that in a well-functioning and rational stock market changes in stock prices reflect both
revised expectations about future corporate earnings and changes in the discount rate at
which earnings are capitalized‖. Current interest rate are used to discount future cash flows
in order determine whether a future investment project is profitable or otherwise. This helps
investors to make informed decisions about where they can put their money or in which
assets class. Economist are still emphasising that the predictive power of the stock market
about economic conditions cannot be disputed as it is evidenced for everyone to see in past
predictions even though the magnitude of the movements cannot be as predicted. It is
expected that present high stock prices have a detrimental effect on future prices of stocks
as expectations are stock prices will be low in future which results in reduction of the pace of
economic growth. Figure 2.16 below shows the JSE share prices for the resources sector
and other sector represented as ―general‖.
-100
0
100
200
300
400
500
600
700
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
R B
illio
n
Purchases Sales Net sale/purchases
44
Figure 2.16: JSE share prices
Source: Author’s computation based on JSE data 2012
2.3.4. Listing requirements
A company that wishes to trade its shares on the JSE must apply for listing. This in turn
improves the tradability of the company‘s shares, which in turn enables it to raise capital
funds from the public either for expansion or acquisition (Goodspeed 2007:118). Exchanges
has legal responsibility towards the public at large for ensuring that order is maintained in the
market, information distribution, transaction guarantee, clearing facilitation and settlement of
transaction as well as the protection of investors. Different exchanges have different listing
requirements. There are also different requirements for listing on the different boards. The
JSE has listing requirements which are built around some general principles which
determines the interpretation of specific requirements should a need arises (Goodspeed
2007:118). There are three choice of board currently available in the JSE in which a
company can be listed on, namely; the Main Board, the Venture Capital Market and the
Development Capital market. A company that wishes to be listed on the JSE in one of these
Boards must comply with the listing requirement. The following are the listing requirements
for the JSE;
The Committee of the JSE must be satisfied that the applicant is suitable and that it is
appropriate for those securities to be listed,
0
50
100
150
200
250
300
350
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Gold
Total
45
All material activities of the issuing should timeously be disclosed to the shareholders
and the general public,
Shareholders must receive full information and the opportunity to vote upon substantial
changes in the issuer‘s business operations and other matters affecting the company‘s
constitution or shareholders rights.
Person disseminating information into the market place must observe a highest standard
of care,
Holders of the same class of securities of an issuer must enjoy fair and equal treatment
in respect of their securities, and
The listing requirements and the continuing obligations should promote investor
confidence in standard of disclosure in the conduct of the issuer‘s affairs and in the
market as whole. (Goodspeed 2007:118)
An application for listing must be made by sponsors and submitted to the Committee of the
JSE. Normally, these are the corporate brokers, investment bank and other professional
advisors. They must also be approved by the JSE and included in the Committee‘s sponsors
before they will be allowed to act as sponsors (Goodspeed 2007:119). The sponsors in
addition to assisting the applicant with application also advise on continuous basis on the
application of the listing requirements and its continuing obligations. The JSE agreement
with the LSE contemplates the creation of a board whose listing requirements will comply
with the listing requirements of the United Kingdom (Goodspeed 2007:119). A SA company
which complies with the UK listing requirements may be admitted to trade on the LSE and
securities which are allowed to trade in this manner are regarded as having primary listing
on both exchanges.
2.3.5. Trading, clearing and settlements
South African Strate authorities, after several initiatives to utilise existing systems in the
country to perform clearing, settlement and depository functions, concluded in mid-1997 that
there was no system in South Africa capable of doing the work required by Strate (Strate
2012). In September 1997 a team of banks and JSE representatives spent time in
Switzerland and concluded that the Swiss system was the right system for South Africa as
Switzerland was one of the few countries to comply with the G30 recommendations and in
particular to achieve true Simultaneous, Final and Irrevocable Delivery versus Payment
(Strate 2012). In May 1998, the agreement to buy the Swiss system was concluded and the
system was fully implemented in April/May 1999 and that alone marked the beginning of a
new era in South African settlement due to number of advantages of the new system.
46
SAFIRES (South African Financial Instruments Real Time Electronic Settlement system) and
its corresponding front end system SAFE (SAFIRES Front End) have made the transition
from a paper-based to electronic-based environment possible.
The process of settlement begins at broker level via the JSE's Broker Deal Accounting
system (BDA) which all Equity Market members are required to use. Settlement on the JSE
currently occurs on a T+5 bases, but is contractual and guaranteed. However, initiatives
have been set in place to assist the migration of the settlement cycle from T+5 to T+3 (JSE
2012). The system facilitates trade confirmation, clearing and settlement of trades between
members and their clients, back office accounting as well as compiling client portfolio
statements. Strate Limited is the licensed Central Securities Depository (CSD) for Equities,
ETFs, Money Market, Warrants and Bonds in South Africa. Strate performs electronic
settlement for the JSE on all on-order book and reported trades as well as maintains an
electronic register of all dematerialised Strate approved securities. Strate Ltd is owned by
the JSE, five major banks and one international bank (Goodspeed 2007:126). The JSE
guarantees settlement of all trades done through the Central Order Book, monitors
settlement of reported transactions and takes necessary actions as defined in the JSE's
Rules and Directives to ensure that settlement takes place. From 2003, the JSE outsourced
settlements of all on-market trades, including all listed equities and warrants to Strate Ltd. In
2003, Strate merged with UNEXcor and Central Depository Ltd (CDL). CDL provided
settlement and depository services for all government debt and UNEXcor was a clearing
house for the Bond Exchange of South Africa. As a result of the merger, the bonds and
money market were to both settle via Strate (Goodspeed 2007:126).
There are also two processes which are involved in settlement ―on market settlement
process‖. This process begins with the investor, who will place an order for trade with a JSE
Broker. This trade is classified as being an ―On-market trade‖. The JSE Broker enters the
order into TradElect, where it will be matched automatically with an opposite order. The
matched trade will then be passed from TradElect, for Broker-to-Broker trades, or BDA, for
Broker-to-client trades, to SAFIRES, the processing system of Strate. SAFIRES will send
instructions to CSDPs to settle. The other process is called ―off market settlement process‖.
Off-market trades are: ―Trades in uncertificated securities not concluded through the
TradElect system and which are reported by the seller and the purchaser of the
uncertificated securities to their relevant CSDP, for settlement through the CSD‖ (Strate
2012). CSDPs through a ―commit‖ process, confirm to SAFIRES that settlement may
proceed. The commit process is a conditional undertaking by the CSDP to ensure that the
transaction will settle on settlement day, meaning that the securities and/or funds are
47
available, on settlement day, to effect the transfer of ownership (Strate 2012).
On settlement day, SAFIRES confirms the availability of securities through the ―reservation
process‖. If reservation at CSDP level is successful, SAFIRES proceeds to send a request
for the transfer of funds to the South African Reserve Bank (SARB). SARB facilitates the
movement of cash between the Participants through the South African Multiple Option
Settlement system (SAMOS). Cash obligations are netted across transactions, per
Participant, per payment run (Strate 2012). Once the availability of bank funds has been
confirmed, and money has been transferred between SARB bank accounts at CSDP level,
SAFIRES will transfer ownership within CSDP uncertificated securities accounts in the
SAFIRES system. For transactions that do not involve payment (e.g. account transfers and
free of value orders), transfers will be effected on settlement date provided the Participants
have sufficient securities balances. Confirmation of a successful settlement is then related to
the CSDP, who reflect the entry in its books at client level. Settlement is completely secure
because the transfer of funds and securities happens simultaneous in a contractual
transaction that is considered to be both final and irrevocable. At the end of the business
day, transactions that could not settle (either due to lack of security or funds) will be treated
as failed (Strate 2012).
2.3.6. South African Financial Market regulations
In 2010 the JSE was rated by the World Federation of Exchanges as the number one stock
exchange in terms of regulation by the World Federation of Exchanges (WFE). The
Securities Services Act No. 36 of 2004 (SSA) took effect on 1 February 2005. It governs the
regulation of securities services in South Africa to include securities exchanges, central
securities depositories (CSDs), clearing houses, and their respective members. It
consolidated the South African regulatory framework for capital markets and aligned the
regulation and supervision of South African financial markets with the prevailing international
developments and regulatory standards. The SSA does not apply to collective investment
schemes regulated by the Collective Investment Schemes Control Act No. 45 of 2002, or
activities regulated by the Financial Advisory and Intermediary Services Act No. 37 of 2002.
Strate is currently licenced as SA‘s only CDS in terms of SSA and SSA provides the legal
framework to support electronic securities services performed by Strate as the CDS. It is
argued that though SSA sets out a framework for market regulation, the detailed substantive
provisions is left to the secondary legislation. Strate is required to issue and amend rules
within the framework of SSA in which the principle is to protect the public interest and
48
provide principles for the supervisory approach adopted by the Registrar of Security
Services. SSA provides a framework for Strate when issuing Rules and Directives. FSB
fulfils the function of a Registrar and the capital markets department within FSB ensures that
the objectives of SSA are met by Strate in its function as a Self-Regulatory Organisation.
The SSA act compels Strate to act with due regard to the right of participants, clients and
issuers. The legislation establishes a co-regulatory regime in terms of which Strate self-
regulatory responsibilities arise. Strate must regulate its activities and those of participants
by making and enforcing the rules that comply with requirements prescribed by the SSA. In
turn, the FSB supervise compliance with SSA by every regulated person (Strate corporate
profile).
2.3.7. Introduction of the Financial Markets Bill
The regulatory environment in the SA financial markets is currently undergoing major
transformation. There is a Financial Markets Bill which is at a final stage after proper
consultation with all relevant stakeholders in the financial markets has been concluded. The
Main objective of the Bill is to correct references to legislation repealed or replaced
subsequent to the enactment of the Securities Services Act in 2004. National Treasury
argued that it was in the interest of simplicity and legal certainty that it was deemed
appropriate to replace the SSA with the FMB rather than propose a complex amendment bill.
The FMB is also argued to be a product of various processes including consultation with
SROs, legislative developments in the country, global financial markets crises and the G-20
recommendations.
The Financial Markets Bill, 2012 (the Bill) replaces the Securities Services Act No. 36 of
2004 (SSA) that has governed the regulation of securities services in South Africa since
2005 and primarily focuses on the regulation of securities exchanges, central securities
depositories, clearing houses and their respective members. The SSA consolidated the
South African regulatory framework relating to capital markets and aligned the regulation
and supervision of South African financial markets with the then prevailing international
developments and regulatory standards (NT Explanatory memorandum on the FMB 2012:
2).
According to National Treasury memorandum, the Bill gives effect to the outcomes of a
regular review of the SSA, which subsequent to its enactment, had not been subjected to a
comprehensive review to assess if it was still sufficiently robust to meet its objectives and the
objectives of securities regulation in general. Additionally, the developments in the local and
49
international financial markets, both pre and post the global financial crisis, as well as other
implementation challenges necessitated a rigorous assessment of the SSA to assess the
appropriateness and effectiveness of the regulatory approach and framework provided for in
the SSA.
According to the National Treasury Policy document on the financial market bill, the Bill is
aimed at strengthening the SRO regulatory model (which has proven efficient and effective
in delivering on the objectives of securities regulation), aligns financial markets regulation
with international best practice, and gives effect to recommendations made by the 2008
World Bank and International Monetary Fund Financial Sector Assessment Programme, as
well as South Africa‘s commitment to the UNIDROIT Convention to improve investor
protection in cross-border transactions (NT: Policy Document Explaining the Financial
Market Bill 2011: 46). To further empower the FSB, the bill strengthens its regulatory
independence and enables it to publish the detail, status and outcome of inspections
and onsite visits. The Bill also enables the FSB to prescribe fees for the SRO and
approve listings requirements and liquidation orders. It provides the rules with which
SROs must comply, as well as the parameters of regulatory requirements that inform
how an SRO must license and supervise its users. It extends reporting requirements by
the SROs to the registrar on matters of systemic relevance, and similarly extends the
reporting requirements of the registrar to the Minister on these matters. The bill also
strengthens governance requirements for SROs (NT: Policy Document Explaining the
Financial Market Bill 2011: 47).
The bill also aims to reduce regulatory burden by facilitating partial membership, meaning
that an entity subject to equivalent regulatory requirements under this Bill and another Act,
can apply for a limited licence under this Bill that exempts that entity from those duplicated
requirements (and supervision). The is aimed at improving investor protection and reduces
systemic risk by increasing the scope of regulation for unlisted securities (to include over-
the-counter derivatives), and enhances transparency in these instruments by providing for
the establishment of a trade repository to which all trades in these instruments will be
reported and monitored. The initial focus of the trade repository will be on OTC
derivatives, in line with the G-20 recommendations. The aim is to have all transactions in
OTC derivatives reported to the trade repository and disclosed to the registrar and other
relevant supervisory bodies to enhance transparency in this market, as well as for risk
identification/assessment and market surveillance purposes. The FMB provides and
allows for the establishment of an independent clearing house as a stand-alone SRO,
50
consistent with what is allowed for exchanges and securities depositories. This provision
envisages the promotion of a central clearing of over-the-counter derivatives, which is
currently under serious scrutiny by the G-20 agenda (NT: Policy Document Explaining
the Financial Market Bill 2011: 48).
To increase competition and better regulate cross-border transactions, the bill provides
for foreign entities to be members of the South African financial markets infrastructure. It
creates a platform for the signing of Memoranda of Understanding with regulators in
other countries, which will help in a situation whereby the FSB wants to investigate,
inspect or conduct on-site visits for foreign regulators. By facilitating settlement
transactions between international and domestic depositories, the Bill improves investor
protection for intermediated security services and helps investors participate in foreign
markets without the need to involve a foreign participant or a global custodian. The FMB
aligns financial markets regulation to the new Companies Act. To safeguard financial
sector stability, the bill ensures that regulators with jurisdiction over industry participants
covered by this Bill may only make decisions on such participants in coordination with
the FSB as lead regulator. Given that the financial services sector is generally held to
higher standards than most sectors with regard to market conduct and consumer
protection, the Bill proposes more stringent regulation to apply to securities markets, and
the regulated activity is therefore excluded from more general legislation like the
Consumer Protection Act (CPA) of 2008 (NT: Policy Document Explaining the Financial
Market Bill 2011: 49). All the changes from the SSA Act of 2004 to the FMB are aimed at
aimed at improving the regulatory environment in the South African Financial markets.
This move will provide a safer environment for raising capital in SA and boost the SA
economy.
2.4. Summary of the chapter
The purpose of this chapter was to analyse the trends in the developments of the South
African Bond and Equity markets. These analyses were in terms of legislations, regulations
and any proposed amendments in the functions and operations of the two markets. This
chapter further highlighted on the performance of the two markets in more or less the same
manner. In trying to respond to one of the objectives of the study (to examine the trends in
the performance and liquidity of the South African equity and bond markets between the
period 2000 to 2008), this chapter examined the trends and the development of these two
markets. Legislative changes like the introduction of the Financial markets Bill (FMB) has
51
also been highlighted with a view of ascertaining how the market and its regulation of
participants evolved over time.
52
CHAPTER 3
LITERATURE REVIEW
3.1. Introduction
This chapter of the thesis provides a definition of liquidity, review of theoretical framework
and empirical literature. It essentially consists of three parts: The first part is the definition,
followed by the theoretical framework while the third part provides a review of empirical
literature. The definition of liquidity is founded on the premise that the execution of
transaction, whether large or small should have no impact the market prices. Importantly,
after liquidity is defined, some measures of liquidity, which are the transaction coast
measure, volume traded based measure, equilibrium price based measure, and the market
impact measures are briefly defined. Because bonds and equities are part of the different
assets classes, this section will also highlight on the assets pricing theory. This section
further highlights a few liquidity theories such as the market liquidity and investor sentiments,
the Amihud and Mendelson clientele effect and the DGP model. The last part of this chapter
will be the discussions on empirical literature review regarding the co-movements of liquidity
in the bond and equity markets. In this section, focus will on linking this this study with the
on-going debates about the bond and equity markets linkages. Focus will be on comparing
and contrasting different findings and empirical work about the bond and equity markets
linkages.
3.2. Definitions
Different scholarly articles define liquidity differently. Benić and Franić (2008:478) highlighted
that, it is ―....due to its multi-dimensional characteristics that there is no single measure that
can capture all aspects of liquidity‖. According to Kapingura and Ikhide (2011: 7), they
acknowledged a different number of liquidity definitions like; Gravelle (1998) who defines
liquidity as being ―the ease with which large-size transactions can be effected without
impacting market prices, and Borio (2000) who on the other hand describes a liquid market
as one where ―…transactions can take place rapidly and with little impact on price‖.
Kapingura and Ikhide also referred to, and adopted the definition by the Committee on the
Global Financial System (CGFS) (1999) who argued that the concept of liquidity can be
further elaborated in a number of dimensions. These include tightness which is the low
transaction cost, depth/size which is the existence of abundant orders, resiliency which is the
quick flow of orders to correct order imbalances, and immediacy which refers to the speed at
which transaction can be executed (Kapingura and Ikhide: 2011:7).
53
The importance of liquidity in financial markets cannot be underestimated since the absence
of liquidity for an asset implies difficulty in converting those assets into cash, and generally
reduces incentives to hold the asset, unless a countervailing premium is offered. Some
articles have since linked the importance of liquidity to markets as oxygen is to humans only
noticeable by its absence. Mensah (2003: 73&74) asserted that liquidity measures can be
classified into four categories:
Transaction cost measures capture costs of trading financial assets and trading
frictions in secondary markets;
Volume-based measures distinguish markets by the volume of transactions
compared to the price variability, primarily to measure breadth and depth;
Equilibrium price-based measures try to capture orderly movements towards
equilibrium prices to mainly measure resiliency; and
Market-impact measures attempt to differentiate between price movements due to
the degree of liquidity from other factors, such as general market conditions or arrival
of new information to measure both elements of resiliency and speed of price
discovery.
The majority of studies seem to have a consensus view about the characteristics of liquid
markets, and it is argued that liquid markets tend to exhibit five characteristics i) tightness ii)
immediacy, iii) depth, iv) breadth and v) resiliency. According to the work of Mensah, which
is also consistent with the work of Benić and Franić (2008:479&480);
Tightness refers to the manner in which transaction costs are low, for example, the
difference between buy and sell prices, like the bid-ask spread in quote driven markets, as
well as implicit costs should be low. Immediacy refers to the speed with which orders can be
processed and, in this context also, settled, and thus reflects, among other things, the
efficiency of the trading clearing and settlement systems. With regards to depth, it refers to
the existence of plentiful orders, either actual or easily uncovered of potential buyers and
sellers, both over and under the price at which a security now trades. Breadth refers to the
manner in which orders are both numerous and large in volume with minimal impact on
prices. Resiliency is a characteristic of markets in which new orders flow speedily to correct
order imbalances, which tend to move prices away from what is warranted by fundamentals
(Mensah: 2003: 74).
54
3.3. Theoretical literature
Stock and bond prices are the discounted sums of the assets future cash flows. Ceteris
paribus, assuming that there are no default risks, a stock‘s cash flow is an infinite stream of
uncertain dividends, while a bond‘s cash flow is a fixed number of payments of pre-
determined coupon income. Theoretically, factors that exclusively affect the discount rates
are likely to move stocks and bonds in the same direction, while those affecting only stock
dividends will reduce their comovements. There are three main theories that explain the
liquidity in bond and equity markets and this section provides a brief discussion of these
identified set of theories.
3.3.1. Assets pricing theory
Asset pricing theory postulates that in a frictionless market, two assets with identical cash
flows should have the same price. If this were not the case arbitrage profits could be easily
realised, however, there is considerable evidence that across a variety of asset classes,
securities with the same cash flows can have different prices. However, Hibbert et al. (2009:
10), referred to the work of Amihud et al. (2005) who discussed a number of pricing models
of increasing sophistication in which the simple premise of frictional costs leads to the
implication that prices must be adjusted downwards and returns adjusted upwards to
compensate investors for bearing illiquidity. This is also consistent with microstructure theory
which states that, where market frictions (i.e. trading costs) exist, assets which are more
expensive to trade will sell at a discount. This discount, expressed as a liquidity premium,
will depend on; the anticipated size of dealing costs and the expected dealing intensity of the
marginal trader (Hebert et al: 2009:8). The demand for liquidity will be influenced by
―clientele effects‖, i.e. the existence of investors with different needs for liquidity. The
expectation is that liquidity effects will manifest themselves in observable proxies. Liquidity
proxies and the clientele effects will be discussed in more detail in the following sections.
Amihud et al (2005:4) highlighted that, the standard asset pricing theory is based on the
assumption of frictionless (or, perfectly liquid) markets, where every security can be traded
at no cost all of the time, and agents take prices as given. The assumption of frictionless
markets is combined with one of the following three concepts: no arbitrage, agent optimality,
and equilibrium. However, this theory is criticised on its assumption of frictionless markets
because, if the market is not frictionless, the theory is shaken and its validity is disputed.
Cochrane and Hansen (1992: 117&118) also criticised the notion of a frictionless market by
arguing that ―....asset markets do not function exactly as described by this paradigm
because, at some level of inspection, market frictions such as transaction costs, short sale,
55
and borrowing constraints must be important. However, the theory is supported due to the
fact that the assumption of frictionless markets is crucial, considering the basic principle of
standard asset pricing; securities, portfolios, or trading strategies with the same cash flows
must have the same price. This simple principle is based on the insight that, if securities with
identical cash flows had different prices, then an investor could buy with no trading costs the
cheaper security, and sell with no trading costs the more expensive security, and, hence,
realize an immediate arbitrage profit at no risk.
Amihud and Mendelson (2005:274) highlighted that the building blocks of the standard
assets pricing theory can also be derived from agent optimality, i.e. if an insatiable investor
trades in a frictionless market, his optimal portfolio choice problem only has a solution in the
absence of arbitrage, otherwise the investor will make an arbitrarily large profit and consume
an arbitrarily large amount. However, it is due to the fact that alleviating friction is costly,
hence, if frictions did not affect prices then the institutions that alleviated the frictions would
not be compensated for doing so. Therefore, no one would have an incentive to alleviate
frictions, and, hence, markets cannot be frictionless. From these analyses, one can also
argue that there will be always friction in the market which will be reflected in security prices.
According to Amihud and Mendelson (2005: 275), this notion was also supported by
Grossman and Stiglitz (1980) who also use a similar argument to rule out informational
efficient markets: market prices cannot fully reveal all relevant information since, if they did,
no one would have an incentive to spend resources gathering information in the first place.
Hence, investors who collect information must be rewarded through superior investment
performance. Therefore, an information difference across agents is an equilibrium
phenomenon, and this is another source of illiquidity.
3.3.2. Market liquidity and investor sentiments
Liquidity is argued to affect expected returns; this is well documented in theory. Baker and
Stein (2003) highlighted that this is due to the fact that investors anticipate to sell their
shares at some point in the future, and recognize that when they do so, they will face
transactions costs. These costs can stem either from the inventory considerations of risk-
averse market makers or from problems of adverse selection (Baker and Stein: 2003: 272).
However, the authors argued that in either case, when the transactions costs are greater,
investors rationally discount the asset in question by more.
The authors developed a theory which explains the connection between liquidity and
expected returns. Their focus was on understanding why there is time-variation in liquidity,
56
either at the firm level or for the market as a whole, which might forecast changes in returns
(Baker and Stein: 2003:272). In their model, two assumptions were made, one about market
frictions and the other about investor behavior. With respect to the former, the authors
assumed that there are short-sales constraints and with respect to the latter, they assumed
the existence of a class of irrationally overconfident investors. They thought of
overconfidence as a tendency to overestimate the relative precision of one‘s own private
signals. Their theory further postulates that ―....when overconfident investors receive private
signals, they tend to overweight them; this leads to ‗‗sentiment shocks‘‘ that can be either
positive or negative. Secondly, when overconfident investors observe the trading decisions
of others, they tend to under-react to the information contained in these decisions, since they
(erroneously) consider others to be less well-informed than they are‖ (Baker and Stein:
2003:272). Accordingly, this aspect of overconfidence lowers the price impact of trades, thus
boosting liquidity generally. The authors supported their theory by further highlighting that, at
some initial date, the irrational investors receive private signals about future fundamentals,
which they overreact to, generating sentiment shocks. The short-sales constraint implies that
irrational investors will only be active in the market when their valuations are higher than
those of rational investors.
In a nut shell, the theory by Baker and Stein is helping in analysing liquidity and the behavior
of the markets in that, it argues that ―....in a world with short-sales constraints, market
liquidity can be a sentiment indicator. An unusually liquid market is one in which pricing is
being dominated by irrational investors, who tend to under-react to the information embodied
in either order flow or equity issues. Thus high liquidity is a sign that the sentiment of these
irrational investors is positive, and that expected returns are therefore abnormally low‖
(Baker and Stein: 2003: 296).
3.3.3. Clientele effects and liquidity policies
Hibbert et al (2009), borrowed from the work of Amihud et al (2000) in discussing the
clientele effects and liquidity. This theory considers the possibility of clientele effects
whereby different groups of investors have different expected holding periods, modelled by
different probabilities of selling up and leaving the market. It is argued that the clientele
effects have a role in determining liquidity premium in financial markets as different investors
have different investment horizons and differing needs when it comes to the ability to
liquidate assets at any point in time. Consequently, this theory postulates that it is common
to stylise investors into two extreme classes: ―buy-and-hold‖ investors and ―mark-to-market‖
investors.
57
The clientele effects was first suggested by Amihud and Mendelson in 1989, where the focus
was on analysing the asset-pricing model which focuses on the role of illiquidity, measured
by the bid-ask spread. In their model (A-M's model), they argued that ―....assets have bid-ask
spreads which reflect their transaction (or illiquidity) costs, and investors have
heterogeneous liquidation plans or holding periods‖ (Amihud and Mendelson: 1989: 480).
The basic intuition behind the model is that rational investors select assets to maximize their
expected return net of trading costs, and, in equilibrium, higher-spread assets are allocated
to investors with longer holding periods which they termed as the "spread clientele". As a
result, the market-observed expected return is an increasing and concave function of the
(percentage) bid-ask spread.
The model also argues that the bid-ask spread is also related to the number of investors
holding an assets which reflects the availability of information about it. Amihud and
Mendelson (1989) also referred to the work of Demsetz (1968), who also found that a larger
number of shareholders bring about a narrower spread and the transaction volume (volume
traded) was also found to be highly correlated with the spread. Amihud and Mendelson
(1989:480) also highlighted that bid-ask spread also decreases when more information is
publicly available about the asset since market makers set the spread so as to compensate
for their losses to better informed investors. Thus, the incompleteness of public information
about an asset is a factor in asset pricing and is reflected in its bid-ask spread. The bid-ask
spread is also related to the residual risk, which may serve as another measure of
incomplete information. Amihud and Mendelson also highlighted on the work of Benston and
Hagerman (1974) who suggested that the residual risk reflects price response to firm
specific information, and is positively associated with insiders' opportunities to profitably
trade against dealers (1989:480):. Referring to the work of Stoll and Whaley (1983), Amihud
and Mendelson further asserted that risk-averse market makers charge a higher spread on
assets with higher variance to compensate for the risk of their stock positions.
3.3.4. The Duffie, Gârleanu, and Pedersen (DGP) Model
Das et al (2003: 3) suggested that, a distinctive feature of most fixed income markets is that
trading takes place over the counter in an environment dominated by a limited number of
dealers. This means that finding a buyer for a given position can be time consuming and
risky, as often there will be no market maker who is committed to providing liquidity.
According to Das et al (2003), this prompted the development of the new DGP model by
Duffie, Gârleanu, and Pedersen in 2003, which takes into consideration the feature of OTC
markets into account. In the model, they structure the process of buyers meeting sellers as a
58
search and bargaining game. In the simplest version of their model, agents in the economy
differ along two dimensions. First, some may incur a cost of holding illiquid assets (called
low-type), whereas others do not (called high-type). The type of the investor is subject to
uncertainty and DGP choose to model it as a two state Markov chain and it is assumed that
some are endowed with the illiquid asset. When two agents meet, they will trade, if doing so
is mutually beneficial, which is the case when owners of the asset who bear holding costs
meet high type agents who do not hold the asset.
According to Das et al (2003:3), the equilibrium price is depended in an intuitive way, on the
parameters of the model. For example, the price will be lower if the probability of a low type
agent switching to a high type agent decreases, if it becomes harder to meet buyers with
whom a profitable trade can be executed, the higher the bargaining power of the buyer, and
finally, the more likely it is that the high type buyer becomes subjected to holding costs.
However, the model is then extended to accommodate risk aversion so that gains from trade
derive from hedging benefits. In this more general setting, risk aversion and volatility both
contribute to increased illiquidity discounts.
3.4. Empirical literature review
The empirical liquidity relationship between the stock and bond markets has been of
considerable interest to economists, policymakers, and investors over the last few decades.
The interest from the different economic agents includes; economists who are interested in
understanding the mechanisms that link these markets. This also includes regulators who
through such understanding, aims to improve the markets' information aggregation and
capital allocation functions and their robustness to shocks to the financial system. Other
interested economic agents include investors who are interested in knowing the return and
diversification properties of major asset classes. This section analyses empirical findings on
the comovements that has been documented between these two markets. It furthers
includes links with other markets using different parameters.
3.4.1. Bond and equity market liquidity- market to market linkages
In recent literature, determining whether liquidity is correlated across financial markets has
become an area of interest in most countries, particularly developed countries. Chordia et al.
(2003) study the common determinants of liquidity in stock and Treasury bond markets. The
study by Chordia et al focuses on the United States of America and data was obtained from
the New York Stock Exchange. Their findings highlight that stock and bond market liquidities
closely mimic each other in terms of their calendar effects. Volatility shocks seem to predict
59
liquidity movements both within and across markets. Liquidity and volatility shocks are found
to be positively and significantly correlated across stock and bond markets implying that the
shocks are often systemic in nature. The authors provide evidence of linkages between
microstructure liquidity and macro-level liquidity as captured by monetary policy changes
and mutual fund flows. Any surprises in bond fund flows are informative in future liquidity for
stock and bond markets. Consistent with Chordia et al (2003), Goyenko (2007) also found
that liquidity has a cross-market effect, which is attributed to trading activity across markets.
They also showed that stock returns contain not only an illiquidity premium of the stock
market as has been documented in the literature, but also an illiquidity premium of the bond
market.
In empirically analysing the cross-market liquidity effect (diagnosing cross-market liquidity
premium), Goyenko (2007:15) found that ―....bond illiquidity significantly affects stock returns,
and stock illiquidity has a significant effect on bond prices‖. The study used data obtained
from NYSE/AMEX/NASDAQ stock portfolios. The results were consistent with ―flight-to-
liquidity‖ from the stock market to the bond market, since bond prices were found to increase
in response to increases in stock market illiquidity. The study by Goyenko also discovered
that the effect of bond illiquidity on stock returns was negative and he attributed that to the
effect of monetary tightening on stock returns. Importantly, the study also highlighted that the
implications of bond illiquidity might be a channel through which macroeconomic shocks are
transferred to the stock market.
Goyenko and Ukhov (2009) also studied liquidity linkages across markets using data
obtained from the New York Stock Exchange. Their paper was aimed at establishing liquidity
linkage between stock and Treasury bond markets. They empirical found that ―there is a
lead-lag relationship between illiquidity of the two markets and bidirectional Granger
causality‖ (Goyenko and Ukhov: 2009: 189). The effect of stock illiquidity on bond illiquidity
was also found to be consistent with flight-to-quality or flight-to-liquidity episodes. Monetary
policy was also found to have an impact on illiquidity where their empirical evidence
indicated that bond illiquidity acts as a channel through which monetary policy shocks are
transferred into the stock market. The conclusion was that, there was evidence of illiquidity
integration between stock and bond markets. The study also concluded that ―....while stock
and bond market illiquidity share many similarities (and reflect the ability to buy or sell large
quantities of an asset quickly and at low cost), they have different economic natures and
bond illiquidity was found to be quick in capturing the effect of monetary policy variables,
while this effect argued to take longer for stock illiquidity‖ (Goyenko and Ukhov: 2009: 211).
60
Chordia et al (2003:1), analysed liquidity co-movements between the bond and equity
markets using cross-market dynamics in liquidity, which are documented by estimating a
vector autoregressive model for liquidity (bid-ask spreads and depth), returns, volatility and
order flow in the stock and bond markets. In their study, innovations to stock and bond
market liquidity and volatility are found to be significantly correlated, implying that common
factors drive liquidity and volatility in both markets. Volatility shocks were found to be
informative in predicting shifts in liquidity. During crisis periods, monetary expansions are
associated with increased liquidity. They also found that money flows to the government
bond sector play an important role in forecasting bond market liquidity.
Chordia et al (2001) also studied the common determinants of daily bid-ask spreads and
trading volume for the bond and stock markets over the 1991-98 periods. For the bond
market, the study by Chordia et al employed data from US GovPX, Inc. and for stock market,
the study employed data from the NYSE. They found that liquidities in stock and bond
markets are codetermined; returns, bid-ask spreads, and volume in one market affect the
bid-ask spread and volume in the other market (Chordia et al: 2001:21). Their results are
generally consistent with asset allocation strategies being conducted simultaneously in both
stock and bond markets in that; a declines in the bond market induce are positively
associated with stock spread changes after controlling for the contemporaneous stock
market return. Their work also highlighted that stock and bond market bid-ask spreads can
be forecasted to a remarkable degree using publicly available variables. Lagged market
returns, lagged interest rates, the lagged bid-ask spread and lagged volume are strong
predictors of the bid-ask spread and volume changes in both markets. A notable result is
that bond spreads lead stock spreads. The results from the 2001 study by Chordia et al is
also consistent with order imbalances due to portfolio reallocations being reflected first in the
institution-dominated bond markets, followed by stock markets. The result also indicates that
asset allocation strategies in periods of enhanced liquidity should be executed first in the
bond market.
In another empirical setting, Engsted and Tanggaard (2000) modified the Shiller and Beltratti
(1992) VAR method in analysing the joint behavior of the Danish stock and bond market
over the period 1922 to 1996. Instead of using the bid-ask spread and volumes as in Chordia
et al (2001), the authors used returns for both the stock and bond markets. The author in
their method used the bootstrap simulation to correct for small-sample bias inherent in VAR
parameter, and they also computed a standard errors and confidence intervals for the
various statistics (Engsted and Tanggaard 1999:3). Instead of capturing the movements by
comparing stock prices and bond yields, the authors compared the movements in the
61
dividend price ratio with the movement long-short yield spread. Data for analysis was
obtained from the annual Danish data from Lund and Engsted (1996) the value weighted
portfolio of individual stock chosen to obtain maximum coverage of the market index was
taken from the Copenhagen Stock Exchange. For bond market data, the authors used the
yield to maturity on long term coupon bond.
The study empirically found that, ―….one year excess stock and bond returns were found to
be positively correlated; however, the simple present value model could not explain the
positive correlation. Over the whole sample period 1922 to 1996 and the smaller period 1922
to 1982, most of the variations in excess returns were due to news about future dividends.
After the world war, from 1947 to 1996, news about future excess stock returns were found
to be the dominating force behind the stock return movements. The findings were also
robust in that news about future long term inflation was found to be the primary cause behind
movements in excess bond returns. Increases in long tern inflation expectations was found
to be either no news or bad news for the stock market in the short run but good news for the
stock market in the long run but news about higher future inflation were found to be bad
news for the bond market both in the short and long run. As a result, news about future stock
returns and bond returns were found to be negatively correlated‖ (Engsted and Tanggaard
2000:3-4).
In another empirical setting, Baele et al (2007) studied the economic sources of bond-stock
return comovements and its time variation using a dynamic factor model. The authors also
used structural and non-structural vector autoregressive models. The authors found that
their fundamental model fails to capture the flight-to-safety phenomenon, as the stock
market uncertainty measures have a highly significant, negative effect on the residual
correlations. These findings were in contrast to the findings of Goyenko and Ukhov (2009),
even though the empirical models of the two studies were different. This was also attributed
to the fact that stock-bond return comovements decrease in times of high stock market
uncertainty and these results were argued to have been consistent with empirical findings by
Connolly, Stivers, and Sun (2005) (Baele et al 2007:31). The study by Baele et al (2007)
also found that ―….there was no significant relationship between innovations in consumer
confidence and residual stock-bond return comovements (Baele et al 2007).
In addition, the authors tested whether liquidity helps explaining stock-bond return
comovements. By regressing the cross product of the residuals from their model on shocks
to proxies of liquidity, they found that the cross product of the stock and bond market
residuals were negatively related to the on/off-the-run spread, indicating that stock and bond
62
returns move in opposite directions when bond market liquidity is low (Baele et al 2007:32).
A positive but insignificant impact of innovations in bond illiquidity on stock- bond return
comovements was also documented and the authors suggested that the ―poor significance
may in part be due to the relatively low quarterly frequency of their dependent variable‖ and
this was also consistent with Goyenko (2007) in that an increases in bond market illiquidity
increase expected bond returns, leading to an immediate drop in bond prices. Goyenko
(2007) also shows that periods of poor bond market liquidity are associated with times of
monetary policy tightening, which is in turn bad news for equity markets. The authors also
revealed that ―….innovations in equity market illiquidity have a negative impact on residual
stock-bond return comovements (Baele et al 2007:33). The empirical findings by Baele
(2007) are also consistent with Goyenko (2007), who found that stock returns decrease and
bond returns increase after a surprise increase in equity market illiquidity. According to
Baele et al (2007), if liquidity is priced in equity markets, an increase in equity illiquidity
raises expected returns, leading to an immediate decrease in stock prices and
simultaneously flight-to-liquidity results in a flow of funds into treasuries, thereby decreasing
yields and increasing returns (Baele et al 2007:33).
Lastly, the authors (Baele et al 2007) performed a multivariate regression of the residual
stock-bond return movement and the results shows that the ―….parameter estimates and
significance levels remained similar‖. When ignoring the interaction effects, the R2's
remained relatively low with a maximum of about 7 per cent. The authors further asserted
that if stock market illiquidity occurs at the same time as bond market illiquidity, the negative
effect of shocks to equity illiquidity on residual stock-bond return comovements should be
mitigated (Baele et al 2007). The authors then include the interaction between stock and
bond illiquidity as an additional regressor and the findings confirmed the negative
relationship between estimated cross product of the stock and bond residuals and shocks to
equity market volatility and illiquidity. They also found a positive and significant liquidity
interaction effect, indicating that when liquidity drops in the equity and bond market, the
stock illiquidity effect is reduced (Baele et al 2007:33).
There are earlier studies which were in investigating the linkages of liquidity in bond and
equity markets. In a similar vein as Baele (2007), Goyenko (2007), Engsted and Tanggaard
(2000), Chordia et al (2001), Campbell and Ammer (1993) studied the comovements of
stock and bond market. The findings by Campbell and Ammer (1993) were contrasted by the
findings of Engsted and Tanggaard (2000). The authors used a log-linear asset pricing
framework and a vector autoregressive model to break down movements in stock and bond
returns into changes in expectations of future stock dividends, inflation, short-term real
63
interest rates, and excess returns on stocks and bonds. Using post-war US data (from
AMEX and NYSE), they found that in the post-war period, bond and stock returns were
practically uncorrelated. Firstly, the authors asserted that ―….the only component which is
common to both assets is the news about real interest rates, but this component was also
said to have relatively little variability. Secondly, a positive correlation between news about
future excess returns on bonds and stocks was documented‖ (Campbell and Ammer
1993:30), this results were also consistent with the work of Fama and French (1989);
however the authors found that the correlation never exceeded 0.4 and that was not
sufficient to produce a large positive covariance between the two asset returns given the
relatively small variability of news about future excess bond returns.
Campbell and Ammer (1993) also documented a ―….weak positive correlation between the
stock return and news about long-term future inflation (the major component of the bond
return). This tends to make bond and stock returns covary negatively, offsetting the positive
covariance coming from the real interest rate and expected excess return effects‖. The
authors further highlighted that the weak correlation of bond and stock returns could be due
to a tendency for equity risk premiums to increase when the short-term real interest rate falls
as also suggested by Barsky (1989) (Campbell and Ammer 1993:30). It is asserted in theory
that if term premiums are close to constant, declining real interest rates would he associated
with a rising bond market but a flat or even declining stock market. However, Campbell and
Ammer (19930 didn‘t find any results supporting this assertion in their empirical setting since
they did not any empirical support that ―….real interest rate changes are important in moving
either bond or stock prices‖ (Campbell and Ammer 1993:32). Lastly, the authors found that
there was a positive correlation between news about real interest rates and news about
equity premiums although the correlation was negative, small and insignificant. Although this
study will employ a mixed bag of time series approaches, it will be close to the Campbell and
Ammer (1993) study in that the macroeconomic variables employed in the empirical model
will be the same. This study will also closely mimic Chordia et al (2001) study in that, the
correlation between these two markets will be examined empirically using the bid-ask spread
and trading volumes for the two markets respectively. However, the difference will be that,
this study will use South African data and the period of the study will also be different.
3.4.2. Cross country liquidity linkages between the bond and equity market
In another study analysing the co-movements in liquidities across markets, De Nicolò and
Ivaschenko (2009) used liquidity indicators which were constructed from value-weighted
price indices in a sample of 30 countries, including G-7, five Australasian industrial countries,
64
a group of emerging markets, and at a global level. They highlighted that in their analysis,
the choice of countries was guided by the availability of pricing data for (at least) stock and
government bond markets. They collected and used available daily and monthly data for the
period from January 1, 1980 to April 31, 2008 on broad stock indices, government bond
indices for all the countries, and money market indices for industrial countries.
To assess whether co-movements between liquidity indicators have become stronger over
time across both equity and bond markets, they found that ―....the coefficient associated with
the time trend for the stock markets regressions was negative and highly significant;
indicating increased correlations of liquidity across these markets and the relevant coefficient
for bond markets was also found to be also negative, but not significant at conventional
significance levels, suggesting a prevalent heterogeneity in government bond liquidity across
countries‖ (De Nicolò and Ivaschenko: 2009: 12). They also argued that based on their
study, there was evidence that a decline in the cross-country variance of government bond
liquidity predicts a decline in the cross-country variance of equity markets liquidity,
suggesting that co-movements in liquidity of connected markets may be mutually reinforcing.
De Nicolò and Ivaschenko (2009: 12) also analysed the extent of the transmission of liquidity
shocks across bond and equity markets and they estimated a simple bi-variate VAR(1) with
the two indicators; bonds and equities monthly data and they find that a systemic liquidity
shock to equity markets results in a decline of liquidity in bond markets, but not vice versa
and accordingly, systemic liquidity shocks in the equity markets were found to be
transmitted to bond markets, while the reverse does not necessarily hold.
Using the data of G7 countries (the U.S., the U.K., France, Germany, Japan, Canada and
Italy) for over 40 years, Li (2002), studied large variations in the stock-bond correlation using
linear regression approach and other time series approaches. Findings revealed a
―….sharply reverting trend in stock-bond correlations across all G7 countries. It grew steadily
upwards from around zero in the early 1960s to about 0.5 in the mid-1990s, and in recent
years they reverted back to zero‖ (Li 2002:27). The correlation also showed a converging
trend in stock-bond correlations across G7 countries. Using a simple model which
endogenously derives stock and bond returns, the study revealed that the uncertainty about
expected inflation and the real interest rate are likely to increase the comovements between
stock and bond returns. However, the effects of unexpected inflation were found to be
ambiguous and it depended on how dividends and the real interest rate respond to
unexpected inflation shocks. Analysing the effect of macroeconomic factors, uncertainty
about long-term expected inflation were found to be playing dominant role in affecting the
65
major trends of how stock and bond returns co-move (Li 2002:28). The study also concluded
that ―….uncertainty about other macroeconomic factors, such as the real interest rate and
unexpected inflation, also affects the comovement of stock-bond returns, but to a lesser
degree (Li 2002:2). The paper by Li also sheds some lights on the reverting trend observed
in G7 stock-bond correlations. The author argued that ―….1970s saw an oil crisis and a
subsequent economic stagflation in major industrial countries, which caused high and
persistent inflation expectations for over a decade; consequently, investors‘ concern about
inflation strongly affected the valuation of financial assets during this period and resulted in
high co-movement between stock and bond returns‖ (Li 2002).
Another study was conducted by Benić and Franić in (2008), even though the study was not
based on the linkages of liquidity across markets, particularly the bond and equity markets,
the study sheds a light on how liquidity levels can be compared in different markets for
different countries. Their study is important because it compares the liquidity levels between
developed and developing countries. The study analysed and measured the levels of
liquidity on the Croatian market and in comparison to other regional markets and one
developed market (Germany) which can be taken as highly liquid. The authors divided the
countries observed in two groups with respect to liquidity levels. In the first group they
include countries that based on their liquidity measure have a high level of liquidity like
Germany, Poland and Hungary. Empirical results suggested that ―....price change in the
index and its volatility do not presume such a qualification, while more complex measures
like turnover rate, ratio of market index price change and turnover rate and suggest that
these markets are more liquid than the others observed‖ (Benić and Franić: 493&494) . The
study also found that the German market, Poland and Hungary were significantly more liquid
than the regional average.
The second group of countries included Croatia, Slovenia, Serbia and Bulgaria. The authors
highlighted the existence of certain variations within liquidity measures for these countries;
however, they undoubtedly imply higher levels of illiquidity compared to the first group of
countries. The study applied illiquidity measure ILLIQ which highlight the illiquidity measure
at a time and the impact of turnover on price change of stocks. This measure of liquidity is
important due to the dimension of liquidity it observes, the depth and the cost of
transactions. Their empirical results also highlighted that, ―....the Croatian market was more
liquid than Serbian and Bulgarian market, significantly more illiquid than German, Polish and
Hungarian, and at the same level of liquidity as Slovenian market (Benić and Franić: 2008:
493&494).
66
Bandopadhyaya (2005), examines the Brady bond (are securities that are issued by a
sovereign in exchange for sovereign debts to commercial banks as a part of debt
renegotiations) market of the two largest Latin American economies, Mexico and Brazil, with
the U.S. stock market being a common exogenous variable to each market. The cross
country results indicated that the stripped yields of each market in the very near future are
determined primarily by the past yields of their respective markets (Bandopadhyaya:
2005:4&5). However, the author found that, over a longer-term horizon the interrelationships
between the bond markets and the stock markets of the two countries become important. In
further analysis, the study found that future yields in the Mexican bond market were affected
by current returns in the Mexican stock market, and to some extent by yields in the Brazilian
bond market (Bandopadhyaya 2005:5). Significant portions of the future variation in the
Brazilian bond market yield were found to be explained by current variation of the yield in the
Mexican bond market and the returns in the Mexican stock market. The Brazilian stock
market returns played a negligible role in both bond markets. The U.S. equity markets, after
controlling for the bond and stock markets in Mexico and Brazil, play an insignificant role in
any of the four markets studied.
In conclusion, Bandopadhyaya (2005:9) examined the relationship between the bond and
stock markets of Mexico and Brazil, two of the main markets in Latin America. Results
indicated that the stock markets of these two countries are independent of each other, with
most of the variations in returns being explained by past returns of each respective market.
The Mexican bond market was also independent of either the stock or the bond market in
Brazil; however, the Mexican stock market affected yields in the Mexican bond market over a
longer-term horizon. The Brazilian bond yields were also found to be closely tied to own
yields in the past but most prominently, the study found that the Brazilian bond yields are
significantly affected by Mexican bond and stock market returns (Bandopadhyaya 2005:9).
Brockman et al (2009:480) noted that previous empirical research finds a common
exchange-level component that influences firm-level liquidity, both in terms of bid-ask
spreads and depths. Although most of the empirical evidence is restricted to firms trading on
a U.S. exchange (Chordia et al. (2000), (2001), (2003), Hasbrouck and Seppi (2001),
Huberman and Halka (2001), Campbell and Ammer (1993)), there is limited evidence of
commonality on non-U.S. exchanges (Brockman and Chung (2002), Fabre and Frino
(2004)). All previous studies that examine commonality in intraday spreads and depths are
single-exchange studies. The study by Brockman et al contributed to literature in three
primary ways. First, the authors conduct the first comprehensive investigation of
commonality in liquidity using intraday spread and depth data from 47 stock exchanges.
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Secondly they examine the impact of global versus local liquidity factors on spread and
depth commonality. Lastly, the authors investigated the determinants of commonality in
liquidity. Given the size and scope of Bloomberg database, the authors were also able to
analyse several aspects of commonality that previous, single-exchange studies could not
address. These unresolved issues included the pervasiveness of spread and depth
commonality, the cross-sectional variation in commonality among exchanges and regions,
the existence of a global liquidity factor, and the impact of macroeconomic announcements
on commonality (Brockman et al :2009:480).
In an empirical analysis, the results confirm that exchange-level commonality is a
widespread phenomenon across the globe. For most exchanges in the sample, the
individual firm‘s bid-ask spreads are significantly influenced by changes in the aggregate
market‘s bid-ask spreads. Similarly, changes in the individual firm‘s depths are significantly
influenced by changes in exchange-level depths (Brockman et al: 2009:480). The cross-
sectional results showed that the emerging Asia stock exchanges exhibit exceptionally
strong commonality in spreads and depths, while the stock exchanges of Latin American
have little if any commonality at the exchange level. After documenting the pervasive role of
commonality within individual stock exchanges, the authors turned their attention to
examining commonality across stock exchanges. They extended the empirical model of
Chordia et al. (2000) in order to measure the impact of changes in intraday global liquidity on
changes in aggregate exchange-level liquidity. The findings represent the first empirical
evidence for the existence of global commonality in spreads and depths. However, the
authors found unambiguous support for the hypothesis that commonality in liquidity spills
over the national border. Movements in aggregate bid-ask spreads and depths on an
individual exchange are significantly influenced by movements in spreads and depths at both
the global and regional level (Brockman et al 2009:481).
Brockman et al (2009), continued with their study by analysing the local (i.e., own exchange)
versus global sources of commonality in liquidity. They compressed all the independent
variables using the modified Gram-Schmidt procedure and reported that the proportion of R2
values contributed by local versus global sources of commonality. The results show that
local sources contribute 38.32% of the typical firm‘s bid-ask spread commonality, while
global sources contribute 19.09%. The results also show that local sources contribute
39.87% of the typical firm‘s depth-related commonality, while global sources contribute
18.76%. Overall, the study found that the local source of commonality plays the dominant
role for both spreads and depths. But even after accounting for local, industry, and regional
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sources of commonality, the study found that the global component contributes from 10% to
15% of the typical firm‘s commonality in liquidity (Brockman et al 2009:482).
The study by Brockman et al (2009) also examined the impact of 2,847 macroeconomic
announcements on commonality in liquidity across the 47 exchanges and finds a significant
increase in commonality for both spreads and depths. The study also investigated the impact
of U.S. macroeconomic announcements on global commonality. Although weaker than
domestic economy announcements, U.S. macroeconomic announcements were found to
significantly increase commonality levels across global markets. In summary, the empirical
findings verify that firm-level liquidity cannot be understood in isolation. Individual firm
liquidity is determined in part by exchange, industry, regional and global commonality
components (Brockman et al 2009:481). The authors however highlighted that, though their
results provide some evidence on the causes of global liquidity co-movements, additional
research will be needed to refine the understanding of the channels through which liquidity
changes in one region of the world affect liquidity changes in another (Brockman et al
2009:481).
In a similar empirical setting as Campbell and Ammer (1993), Shiller and Beltratti (1992)
used dynamic present value model to study the comovements of stock prices and bond
yield. Employing annual data of the U.S. and the U.K in their study, they concluded that the
observed stock-bond correlations are too high to be justified by present value model theory.
The authors concluded, if the assumption of the present value model holds, the nature of the
variability of discount rates and dividends in relation to available information in advance,
there should be a slight negative correlation between stock prices and changes in long term
interest rates (Shiller and Beltratti 1992:18). However, the authors found that the observed
correlation is more negative in the U.S and U.K than what theory proposes. The study by
Shiller and Beltratti (1992:18) also found that excess returns in the stock market covary too
much with excess returns in the bond market. Finally, authors found no evidence of any
overreaction of either the stock or bond market to changes in the inflation rates (Shiller and
Beltratti 1992:19). The findings of the study by Shiller and Beltratti (1992) will be of more
interest in this study in that the effects of inflation changes will be incorporated to analyse the
behaviour of the two markets.
3.4.3. Multiple-Markets liquidity linkages
Another empirical study is that of Fleming et al (1998) who investigated the nature of
volatility linkages in the stock, bond, and money markets. For the empirical analysis, Fleming
69
et al used daily returns on the S&P 500 stock index futures, T-bond futures, and T-bill futures
for the period January 1983 to August 1995 for US markets. The authors first estimated
univariate specifications of the empirical model for each of the three contracts. This analysis
indicated that the model accurately described the time-series behavior of returns for these
markets. The authors then estimated bivariate specifications of the model to measure the
correlations between the log information flows. The correlation estimates were 69% for the
stock and bond markets, 67% for the stock and money markets, and 64% for the bond and
money markets (Fleming et al 1998:114). These results indicate that there are indeed strong
linkages between these markets. However, the study found that the correlations are not
perfect. Furthermore, the result also indicated that the markets do not share the same
information and the concluded that the information spill over caused by cross-market
hedging is incomplete (1998:114).
The study also indicated that the linkages across these markets strengthened following the
1987 stock market crash. This finding suggested that a shift in volatility regimes may
perhaps have been caused by the crash or the adoption of program trading curbs. It was
also consistent with an increase in cross-market hedging due to greater futures market
liquidity in the post-crash period. Overall, the evidence indicated that strong volatility
linkages are a key feature of the stock, bond, and money markets. The study investigated
the role of information in creating volatility linkages between markets and this differs with
other studies like Chordia et al (2003), De Nicole and Ivaschenko (2009) etc., in that it
investigated the role of information volatility as opposed to studying the spreads or volumes
measures of liquidity in the in different markets. To generate predictions about the strength
of these linkages, the authors developed a simple model of speculative trading. Under their
model, two distinct sources of linkages arises, one is common information, such as news
about inflation, which simultaneously affects investor expectations in multiple markets and
the second source is due to cross-market hedging. When information alters expectations in
one market, traders adjust their holdings across markets, producing an information spill-over.
In the stock, bond, and money markets, both of these sources should play an important role.
Each market is influenced by macroeconomic information and the characteristics of these
markets are conducive to cross-market hedging and based on that rationale, the authors
expected to observe strong volatility linkages (Fleming et al: 1998:135).
In summary, Fleming et al (1998) empirical analysis provided support for the simple model of
speculative trading. Under their model, traders consider the correlation of returns in different
markets when forming their speculative demands. This leads them to diversify their holdings
across markets in order to reduce the variance of their speculative profits. This behavior,
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along with the influence of information events that simultaneously alter expectations in
different markets, generates strong volatility linkages between markets. This result has
important implications for asset allocation and risk management strategies that emphasize
time-varying risk and return. Investors have long used models that account for common
factors in returns. Fleming et al (1998) analysis indicated the importance of also accounting
for common movements in volatility (Fleming et al: 1998:136).
Das and Hanouna (2009:113) studied the similarities between liquidity in different markets
and found an inherent dissimilarity between liquidity in corporate bonds and CDS liquidity
based on differences in the market‘s use of these instruments. Data for all the variables were
obtained from Bloomberg. Whereas the average corporate bond does not trade frequently,
and is held for portfolio reasons, default swaps are widely used in credit arbitrage,
construction of CDOs, and risk management. Therefore, even though there is a literature on
liquidity effects in bond spreads (Chen et al., 2007; Goldstein et al., 2006), the authors found
it necessary to investigate the same phenomenon separately in the CDS markets. The
authors investigated whether individual firm liquidity can further explain the cross-section of
CDS spreads, after controlling for default risk, using market-based and firm-specific
variables. The study found strong evidence that CDS spreads contain liquidity components
(Das and Hanouna 2009:114). Using three different proxies for equity market liquidity that
are commonly used in the equity literature, and a one standard deviation change in the
liquidity metric resulted in a 6% to 16% change in CDS spreads. Their paper is also unique
in that, unlike the link already made in the literature between bond spreads and bond market
liquidity, they make the link between CDS spreads and equity market liquidity.
The authors also provide a theoretically supported link between equity markets and CDS
spreads via the mechanism of hedging. The sign and magnitude of the liquidity effect on
CDS spreads is derived analytically in the structural model framework of Merton (1974).
After positing theoretically that equity market illiquidity should be a component of CDS
spreads at the individual firm level, empirical analysis shows that this is indeed so at high
levels of statistical significance (Das and Hanouna 2009:116). The paper further derived that
the illiquidity component will increase as the credit quality of the firm declines. A test to affirm
the results was also explored using equity market proxies for liquidity which has the practical
benefit that plentiful data is available. The results imply a growing connection between the
credit and equity markets, and suggest that cross-market liquidity linkages may be a good
avenue for further research (Das and Hanouna: 2009: 121&122).
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Norden and Weber (2009: 554), unlike Brockman et al, investigated the relationship between
the credit default swap (CDS), the corporate bond and the stock market for an international
sample over the period 2000 to 2002. Their focus was on the intertemporal co-movements,
in particular on lead lag relationships at different data frequencies and on the dynamic
adjustment process between markets. The focus of the study was on European markets.
Firstly, analysing the aggregate and the firm-specific market co-movement, Norden and
Weber found that stock returns are significantly negatively associated with CDS and bond
spread changes. Secondly, stock returns were found to be the least predictable and bond
spread changes the most predictable variable at all data frequencies (Norden and Weber:
2009: 554). Over and above that, at a weekly and daily frequency CDS spread changes
were found to Granger-cause bond spread changes for a considerably higher number of
firms than vice versa.
The study also found that the negative intertemporal relationship between the CDS and
stock market was more pronounced than the one between the bond and stock market and
the sensitivity of the CDS market to prior stock market movements was significantly related
to the firm‘s average credit quality and the size of bond issues but not to firm size. CDS
spread changes from low-grade firms are more sensitive to lagged stock returns than those
from firms with a relatively good rating. There was no such rating dependency for the
sensitivity of bond spread changes to lagged stock returns. Bond spreads changes reacted
more strongly to lagged stock returns the larger the size of bond issues which can be
interpreted as a consequence of liquidity effects in corporate bond markets. For the majority
of firms the study detected cointegration of CDS and bond spreads. Using a vector error
correction model, it revealed that the CDS market contributes more to price discovery than
the bond market which is consistent with findings from Blanco et al. (2005) (Norden and
Weber 2009: 555). Whereas the adjustment process for European firms occurs in both
markets, it mainly takes place in the bond market for US firms indicating the leading role of
the CDS market and a comparison of Granger causality tests for firms with and without
cointegrated spreads confirms that in both groups CDS spread changes Granger cause
bond spread changes for a higher number of firms than vice versa (Norden and Weber 2009:
554). This indicated some liquidity commonality between these markets as well.
Jacoby et al (2009) like Norden and Weber (2009), used data from the credit default swap
(CDS), corporate bond, and equity markets by constructing several measures of liquidity and
examined the spill-over of liquidity shocks across these markets. Since liquidity has multiple
dimensions (tightness, depth, resiliency, immediacy), the authors found it necessary to
construct a number of measures. Based on the principal component analysis of multiple
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liquidity measures, the study shows that there is a dominant first principal component in
each of the markets (Jacoby 2009:21). However, the linkage of liquidity shocks was found to
be varying between the different markets. A common component between the equity and
both the CDS and bond markets was found, but not between the CDS and bond market
(Jacoby 2009:21). Interpreting the vector autoregression results, the study also highlighted
that while there was a spill-over of liquidity shocks between equity and CDS markets,
surprisingly, there was no clear spill-over of liquidity shocks between equity and bond
markets. Furthermore, it appeared that there was a time lag of liquidity spill-over from the
CDS to both bond and equity markets. Finally, the study found no evidence of liquidity spill-
over from bond to CDS market (Jacoby 2009:21).
Fabre and Frino (2004) presented a study using the research design of Chordia et al (2000)
to examine commonality in liquidity for a broad sample of stocks listed on the Australian
Stock Exchange (ASX). In contrast to previous research, there was some evidence of
market-wide commonality in liquidity for ASX stocks, but it was less significant and less
pervasive than that observed in other markets (Fabre and Frino 2004:357). These results
were consistent with explanations based on differences in market structure between the
USA and Australia. Similarly to Chordia et al (2000), the mean value of liquidity variables
was found to be larger than the median, suggesting that they are skewed. While absolute
bid-ask spreads on the ASX were found to be lower than on the NYSE ($A0.03 compared to
$US0.32), the percentage bid-ask spreads of ASX stocks were around three times larger
than those on the NYSE. Depth (in shares) was also larger on the ASX; however, the
average price of ASX stocks was lower than on the NYSE (Fabre and Frino 2004:359).
The study by Fabre and Frino (2004) also reported on the correlation coefficients for the
liquidity variables. Chordia et al. (2000) report correlations between bid-ask spread and
depth of between −0.3 and − 0.39; however, the correlations reported for ASX stocks were
found to be much lower, reflecting the differences between the trading mechanisms of the
NYSE and ASX (Fabre and Frino 2004:357). While the specialist actively controls bid-ask
spreads and depths on the NYSE, no such centralized control occurs on the ASX.
Descriptive statistics for the daily absolute change in liquidity variables reported were shown
and compared with Chordia et al (2000) who suggested that the variation in bid-ask spreads
is almost three times larger on the ASX compared to the NYSE; however, changes in ASX
and NYSE specialist depths exhibit similar variability (Fabre and Frino 2004:363). In a nut
shell, the study by Fabre and Frino (2004) examines commonality in liquidity for a broad
sample of ASX stocks and in contrast to other studies, the study found that, there is some
evidence of market-wide commonality in liquidity for ASX stocks, but it was found to be lower
73
in significance and less pervasive than that observed in other markets such as the NYSE
(Fabre and Frino 2004:363).
3.4.4. Other liquidity measures and linkages across markets
Domowitz and Wang (2002), measured liquidity as a function of the underlying supply and
demand functions, as in Irvine, Benston and Kandel (2000) and Coppejans, Domowitz and
Madhavan (2000). Since supply and demand are most recognizable in a limit-order market,
the authors demonstrate the liquidity measure in this kind of market with the understanding
that theoretically it applies to any market since supply and demand exist everywhere. In their
study, the economic meaning of the liquidity measure is the gap between a stock‘s supply
and demand schedules (Domowitz and Wang 2002:31). The study highlighted that when
supply and demand are far apart, liquidity is low because it is impossible to match orders.
This was also interpreted as a concession that an impatient trader has to make in order to
get order executed immediately (Domowitz and Wang 2002:31). The larger the concession,
the lower the liquidity hence the measure is actually an inverse measure of liquidity.
According to Domowitz and Wang (2002), it is also a size-related (liquidity decreases with
order size), ex ante measure of liquidity that goes beyond the inside quotes.
The paper by Domowitz and Wang (2002) then used functional covariance to measure
commonality in liquidity. The paper shows that the liquidity commonality is due to supply and
demand co-movements, through which order types play an essential role (2002:32). In the
study, order types included market orders and limit orders. Market orders reduce liquidity
and limit orders add liquidity. The paper by Domowitz and Wang also used a simulation
method and the results support that it is order-type commonality, not the order-flow (order
size plus order direction) commonality that drives the commonality in liquidity in markets.
Order-type commonality alone can result in high liquidity commonality, but no return
commonality (Domowitz and Wang 2002:32). Order-flow commonality, on the contrary was
found not to cause liquidity commonality: when order flows are controlled to co-move across
two stocks, as long as their order types are random, no liquidity commonality can be
detected (Domowitz and Wang 2002:32). However, order-flow commonality was found to
cause return co-movement, though it was of a smaller magnitude than intuitions would
suggest.
In the study, when order-type commonality and order-flow commonality were combined
together, both liquidity commonality and return commonality were higher. The paper also
highlighted the fact that the results were contrasting the intuition that order-flow commonality
74
causes liquidity commonality, but that intuition is doomed to fail according to the authors
primarily because what changes liquidity is whether an order reduces liquidity or increases
liquidity (market or limit), not whether it is a buy or sell per se (Domowitz and Wang
2002:32). Order size is argued to affect the magnitude of the change in liquidity, but without
it, order type is enough to give the direction of the change in liquidity. The simulations also
provide an example that stocks with negative correlations in returns can have positive
correlations in liquidity (Domowitz and Wang 2002: 32).
The statement about order flow, order type, liquidity and return commonalities were further
verified by evidence from the Australian Stock Exchange data during 3/1/2000 to 12/31/2000
for the 19 stocks that were consistently in the ASX 20 index. Domowitz and Wang (2002)
first provided two examples of stock pairs that have little return correlations but have
significant positive liquidity correlations. The authors then show that these stocks‘ order-flow
correlations are in line with their return correlations but not with their liquidity correlations and
their order-type correlations are in line with their liquidity correlations but not with their return
correlations (Domowitz and Wang 2002:32). Finally, the authors run two separate OLS
regressions: 1) return correlations on order-type correlations and order-flow correlations, and
2) liquidity correlations on order-type correlations and order-flow correlations for the entire
sample. They found that order-flow correlations dominate order-type correlations in
explaining return correlations, and order-type correlations dominate order-flow correlations in
explaining liquidity correlations (Domowitz and Wang 2002: 32).
All the results from the Domowitz and Wang (2002) study demonstrated that return
commonality and liquidity commonality are not due to the same reason: order type
determines liquidity and order flow determines return. Meaning that, it is possible for stocks
to have negative or little return correlations but strong positive liquidity correlations. If this is
true, then implementing the traditional diversification strategy faces one potential obstacle:
the co-movements in liquidity for stocks that cancel out with each other in returns. Liquidity
commonality makes the diversification benefit hard to realize because it is difficult to trade a
basket of stocks at one time. The paper by Domowitz and Wang (2002) documents the
challenge to the traditional diversification strategy posted by liquidity commonality, but
leaves the severity of the challenge to further study (Domowitz and Wang 2002: 33).
Das et al (2003) suggest that there are three types of news shocks common to bond and
equity markets. These are intra-day calendar effects, public information effects and GARCH
effects. However, Das et al point out that unlike stock and corporate bond markets, the
government bond market is driven mainly by public information or macroeconomic news
75
events. As highlighted in Kapingura and Ikhide (2011), consistent with Das et al (2003)
propositions, it is argued that both domestic and foreign investors will be reluctant to
purchase government securities, especially medium- and long-term instruments, when there
are expectations of high inflation, large devaluations, or high risks of default. Working toward
a macroeconomic policy framework with a credible commitment to prudent and sustainable
fiscal policies, stable monetary conditions, and a credible exchange rate regime is therefore
important as this can be seen from what the South African government and the south African
reserve bank has committed the country to, in terms of exchange rate and monetary policies.
The impact of macroeconomic variables on bond and equity market liquidity cannot be
underestimated; hence these macroeconomic variables will be included in this study.
Liquid capital markets are important in promoting economic growth of a country. This is also
supported by the work of Mensah (2003:6). However, it is also argued by Mensah that ―the
relationship between liquidity of markets and economic growth holds after controlling for
other economic, social and political factors that may affect economic growth such as
inflation, fiscal policy, political stability, education, the efficiency of the legal system,
exchange rate policy and openness to international trade (Mensah: 2003:6). It is further
highlighted that, the transmission of liquidity to economic growth occurs through savings,
investment, and productivity. Countries that had more liquid capital markets enjoyed both
faster rates of capital accumulation and general productivity gains over the past few
decades. Importantly, it is argued that other commonly used measures of market
development, such as market size (measured as market capitalization to GDP), are not
significantly related to economic growth. However, empirical work suggests that, it is not the
size or volatility of the stock or bond market that matters for growth, but the ease with which
these instruments can be traded.
Consistent with Das et al 2003, Kapingura and Ikhide 2011, Nasser and He (2003) state that
macroeconomics variable determines liquidity in bond markets. According to Nasser and He
(2003), investors have become concerned with overall trends than with individual company
fundamentals. Since both stocks and bonds are investment alternatives that compete for the
investor‘s funds, the funds flow from one market to another due to a change in market
situation and macroeconomic factors. Nasser and He pointed out that a number of studies
have reported a negative relationship between long-term government bond rate and the
stock prices in the US and UK. Davis (1999) concurs with Nasser and He (2003) and
revealed movements of the economy and/or of interest rates as of overriding importance in
the purchase of fixed-income securities. A rise in interest rates, due for instance, to
monetary policy tightening may lead to a financial crisis, with liquidity collapses in security
76
markets. In addition, in the presence of uncertainty, adverse surprises may trigger shifts in
confidence, affecting markets and institutions more than appears, thus introducing the
potential for a liquidity crisis.
It has been noted that there is an existence of adverse selection in financial markets which
also impact on the liquidity of securities. As in Kapingura and Ikhide (2011), it is suggested
that adverse selection problems arise when informed traders who possess private
information on the value of an asset not currently reflected in prices, are in the market. Such
traders will want to trade only if the current ask price they face is below or the bid price
above the fundamental value of the asset.
In summary, there are empirically sound economic reasons for expanding capital markets in
different countries for the benefit of those country‘s economies. However, the economic
benefits of capital markets in countries cannot be fully captured unless markets are
sufficiently liquid. Such liquidity requires among other factors, a critical mass of listed
securities, an investor base and trading systems that support speedy execution and efficient
price discovery and of course stable macro and micro economic factors such as prudent
fiscal and monetary policies
3.5. Conclusion
From the empirical literature explored above, it can be concluded that there is a co-
movement of liquidity between markets that can be observed both at theoretical and
empirical levels. It has to be highlighted that the degree of the commonality between liquidity
in different markets is to a certain degree different. However, there seem to be common
factors that drive liquidity across markets. Some of those recent studies have shown that
liquidity can spill-over from one market to another. For example, Chordia, Sarkar, and
Subrahmanyam (2005) document covariation in liquidity and volatility between the stock and
Treasury bond market, while in the same spirit Goyenko (2005) documents a cross-market
effect of liquidity affecting returns in both markets. De Jong and Driessen (2005) show that
liquidity risk from the equity and Treasury market affects corporate bond returns. As much as
these studies investigate the liquidity comovements, most of these studies are based in
developed countries like United States of America (Chordia et al (2000,2003,2005,
Goyenko(2007), Goyenko and Ukhov (2009), Fleming et al (1998)). However, there are few
studies that focus on developing economies like the Latin America (Bandopadhyaya (2005),
who used date from Brazil and Mexico). To the best of my knowledge, there is no study
investigating liquidity linkages in the South African Capital Markets as well as African
77
markets. This then leaves a huge gap in terms of understanding the liquidity dimension in
South African context, even though there few studies for developing economies like the case
of Brazil and Mexico.
As already stated, despite the evidence regarding the commonality in returns and liquidity
within and across stock markets, my knowledge regarding these common patterns mostly in
African markets, is limited thus far and this prompted this study to investigate these liquidity
commonalities in the South African Context. South African literature has not thoroughly
examined this aspect of commonality. Furthermore, little is known why there are these
common patterns. The aim of this paper is to fill those gaps in the literature by employing
different time series approaches.
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CHAPTER 4
METHODOLOGY
4.1. Introduction
This chapter presents the theoretical and empirical framework that will be used to carry out
the empirical analysis of the study. The first section of this chapter presents the theoretical
framework which provides the link between market liquidity and its determinants as well as
the econometric methodology that are employed to estimate the empirical model. In an effort
to establish the linkages between the South African bond and equity market liquidity, the
study will employ different time series approaches which will include; cointegration test,
within the VAR framework, Generalised Impulse Response Functions and Forecasting Error
Variance Decompositions as well as the Granger Causality.
4.2. Theoretical framework
This section focuses on building the theoretical framework from the available literature.
Different analytical framework will be explored which links liquidity in bond and equity
markets as well the behavior of the participating agents. The price behavior in both the stock
and bond markets and even the viability of a market depend on the ability of the trading
mechanism to match the trading desires of sellers and buyers. This matching process
involves the provision of market liquidity. As discussed in chapter 3, the theories that
underpin market liquidity include investor sentiments. This study will also benefit from this
theory in that, when liquidity is high, indicated by large volumes traded, high trade values,
increased foreign investor participation, it will also be an indication of the presence of over-
confident investors in the markets. As suggested by this theory, minimal price impact is as a
result of overconfident investor‘s presents in the market which in turn boasts liquidity as they
over or under react to private signals. However, unlike studies who used the bid-ask spread
as liquidity measure, this study will use the volumes traded, foreign investor participation and
the value traded as liquidity measures. It due to the unavailability of data regarding the bid
ask spread that it will not be utilised in this study.
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4.3. Empirical Model specification
In formulating a model which can help in establishing the linkages of liquidity in the South
African bond and equity market, the study poses two different questions. Firstly, whether
market microstructure factors such as volume,, trade values and returns influence liquidity in
the bond and equity markets. Secondly, to understand whether macroeconomic factors such
as inflation, exchange rate, interest rate, foreign investor participation and stock market
index and bond market affect bond and stock market liquidity. The relationship of both the
bond and equity markets liquidities can be presented in a linear function such that market
liquidity is a linear function of microeconomic and the macroeconomics variables. This can
be represented using the three liquidity measures used in this study and the other micro and
macroeconomic variables of interest in this study; the linear function can be presented as
follows;
And the VAR matrixes which are employed in this study are as follows;
(4.1)
(4.2)
(4.3)
Where represents bond and equity markets liquidity measured by trade values,
volumes and foreign investor participation respectively. Variables which are argued to have
direct impact on liquidity are, volumes of bonds and equities ( ). Volume is
the quantity of equities or bonds and it measures the depth of liquidity in the markets.
Foreign investor participation in bonds and equities ( ), it is the purchase or sale of
bonds and equities in South Africa. It reflects foreigner‘s appetite and perception about the
local financial markets. Trade values in both the bond and equity markets ( ),
this is the total price multiplied by the total quantity of the transaction at a time in both
markets. Interest rate (measured by the repo rate in this study) ( ), this is a monetary
policy tool used by the South African reserve bank to control the level of inflation and liquidity
in the system. It is the discount rate at which a central bank repurchases government
securities from the commercial banks, depending on the level of money supply it decides to
maintain in the country's monetary system. To temporarily expand the money supply, the
central bank decreases repo rates (so that banks can swap their holdings of government
80
securities for cash) vice versa. Inflation rate ( ), it is the general increase in the price
level in an economy. Exchange rate ( ), which is the rate at which the domestic currency
exchange with foreign currency.
Equation (4.1, 4.2 & 4.3) state that market liquidity is a function of macroeconomics and
microeconomics factors. Since this study uses traded values, volumes traded and foreign
investor participation as a measures of liquidity in both markets, , represents
liquidity in the bond and equity markets measured by volumes traded, trade values as well
as foreign investor participation in both markets and the dependant variables will then be a
function of the macroeconomics factors as well as the microeconomic factors of the other
market, vice versa. As advocated by the assets pricing theory, the existence of frictional cost
means prices must be adjusted downwards and returns adjusted upwards to compensate
investors for bearing illiquidity and it can be expected that when prices of bonds and equities
goes down, returns will go up and liquidity will be enhanced, hence the theory assumes a
market where there are no frictional costs. This is also supported by the liquidity and the
investor sentiments theory which argues that the existence of overconfidence investors
result to low price impact and thereby boasting liquidity in general. The effects of expected
returns on liquidity is clearly highlighted in the Amihud et al clientele effects theory which
postulate that investors choose assets that maximise their expected return of the net trading
costs. The micro and macroeconomics variables highlighted in the above equations, all have
direct influence on market liquidity, however, these variables of interest in this study
highlighted in details on the data sections and their expected influence is also highlighted in
that section.
4.3.1. Stationarity Analysis
In analysing the empirical results, the first step is to test for stationarity of all the variables.
Gujarati (2003) suggested that a stationary stochastic process implies that the mean and
variance are constant overtime, and the covariance between two periods depends only on
the lag between the two time periods and not the actual time at which the covariance is
computed. This implies therefore that a non-stationary time series will have a varying mean
or varying variance or both. The statistical and time series properties of the data set will first
be carried out using the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) to test for
unit root. Mallik and Choudhry (2001) and Ahmed and Mortaza (2005) point out that the PP
test can properly distinguish between stationary and non-stationary time series with high
degree of autocorrelation and in the presence of structural break.
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The ADF test is an improvement of the Dickey-Fuller test (DF) which was devised by Dickey
and Fuller (1979, 1981). The improved ADF test gives better results than the DF test as it
includes extra lagged terms of the dependent variable in order to eliminate autocorrelation.
However, Culver and Papell (1997) points out that the ADF as well as the DF tests are
unable to discriminate well between non-stationary series with a high degree of
autocorrelation. It is also argued both the DF and ADF tests may also incorrectly indicate
that the series contain a unit root when there is a structural break in the series. It is also
widely believed that the ADF test does not consider the cases of heteroscedasticity and non-
normality frequently revealed in raw data of economic time series variables. Due to the
limitations of ADF discussed above and to ensure robust results, the Phillips- Perron test
developed by Phillips and Perron (PP) (1988) will be undertaken to check if the results are
consistent with the ADF test. This test allows for fairly mild assumptions concerning the
distribution of errors.
4.3.2. Multivariate vector autoregression and Johansen’s Cointegration test
In trying to explore intertemporal associations between market liquidity; foreign investor
participation ( ), traded values ( ), volume
( inflation ( ) and interest rates measured by the repo rate ( ), the
study employs three vector autoregression (VAR) frameworks which is made up of five
variables in each model: foreign investor participation equities and in bonds, Volume traded
in bonds and equities, values traded in both markets, South African inflation rate and repo
rate and the exchange rate. The VAR is adopted for this particular work because with VAR,
once the variables are cointegrated; it becomes easy to distinguish between the short-run
dynamics and long-run causality. Also the VAR framework eliminates the problems of
endogeneity by treating all the variables as potentially endogenous.
In empirically testing the liquidity linkages between the South African bond and equity
markets, the study will benefit from Chordia et al (2003) and Kapingura and Ikhide (2003).
Volume, foreign investor participation and values traded will be used as proxies for liquidity
in the study in all the three models. According to Chordia et al (2003:14) ―there is also
reason to believe that cross-market effects across stocks and bonds may be significant‖.
This argument is based on the notion that, if there are leads and lags in asset allocation
trades across these markets, then trading activity in one market may predict trading activity,
and, in turn, liquidity in another. Similarly, leads and lags in volatility and liquidity shocks may
have cross-effects. For example, if systemic (macro) shocks to liquidity and volatility get
reflected in one market before another, then liquidity in one market could influence future
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liquidity in another. Thus, insofar as the above variables in one market forecast the
corresponding variables in the other, the preceding arguments carry over to cross-market
effects as well according to Chordia et al (2003:14).
Given that there are reasons to expect cross-market effects and bi-directional causalities,
this study will adopt five variable vector auto-regression that incorporates five variables in
each of the three models as opposed to the eight variables used in Chordia et al (2003) and
Kapingura and Ikhide (2011).
The VAR model for the study is discussed as follows:
Assuming that is the vector of variables, the intra-impulse transmission process of
which is to be captured by the study, the dimension of (that is n) is 5, given the five
variables are considered in the analysis for each of the three models. The general VAR
model can be presented as follows;
Using matrix algebra notations, a 5-variable structural dynamic economic model for the study
can be stated as:
(4.4)
Where is the matrix of the variable coefficients
is the vector of the observations at a time of the variables of the study; vector Y is
defined as (4.5)
And
Where:
83
(4.6)
After establishing the order of integration of all included variables, cointegration tests will
be undertaken using the Johansen approach. Cointegration tests will help in establishing
if there is a long-run relationship between the traded values, foreign investor
participation and volumes traded for the bond and equity markets, CPI, EX (exchange
rate) and the repo rate. If there is evidence of Cointegration between the variables, the
next step will be to estimate the Vector Error Correction Estimates to establish whether
the relationship is positive or negative. However, if there is no evidence of Cointegration
between the variables, the next step will be to estimate VAR in first difference to
establish the whether the relationship is positive or negative.
The Johansen procedure produces two statistics, the likelihood ratio test based on maximal
eigenvalue of the stochastic matrix and the test based on trace of the stochastic matrix.
These statistics are then used to determine the number of cointegrating vectors. The test is
based on an examination of the matrix, where can be interpreted as a long run
coefficient matrix. The test for cointegration is calculated by looking at the rank of the
matrix via its eigenvalue. can be defined as a product of two matrices:
(4.7)
The matrix gives the cointegrating vectors, while gives the amount of each cointegrating
vector entering each equation of the VECM, also known as the adjustment parameter.
Under the maximum Eigenvalue (denoted by ) test the null hypothesis that rank
is tested against the alternative hypothesis that the rank is . The null hypothesis
attests that there is cointegrating vectors and that there are up to cointegrating
relationships, with the alternative suggesting that there are ( vectors.
The test statistics are based on the characteristic roots (eigenvalues) obtained from the
estimation procedure. The test consists of ordering the largest eigenvalues in descending
order and considering whether they are significantly different from zero. If the variables are
not cointegrated, the rank of is zero and all the characteristic roots will equal zero. To test
84
how many of the numbers of the characteristic roots are significantly different from zero, the
maximum eigenvalue uses the following statistic:
(4.8)
The second method is based on a likelihood ratio test about the trace of the matrix and it is
called the trace statistic. The trace statistic considers whether the trace is increased by
adding more eigenvalues beyond the rth eigenvalue. The null hypothesis in this case that the
number of cointegrating vectors is less than or equal to . Just like under the maximum
eigenvalue, in the event that , the trace statistic will be equal to zero as well. On the
other hand the closer the characteristic roots are to unity the more negative is the
term and therefore, the larger the trace statistic. The trace statistic is calculated by:
(4.9)
The procedure to determine the presence of cointegration involves working downwards and
stopping at the value of which is associated with a test statistic that exceeds the displayed
critical value. Critical values for both the maximum eigenvalue and trace statistic are
provided in Eviews.
4.3.3. Generalised Impulse response function and error variance decomposition
To determine the reaction of liquidity to innovations in their determinants, impulse responses
functions will be constructed. Impulse response functions show the effects of shocks on the
adjustment path of the variables. Forecast error variance decompositions measure the
contribution of each type of shock to the forecast error variance. Both computations will be
useful in this study as they assess how shocks to bond liquidity affect equity market liquidity
and vice versa. The IRF analysis is used in dynamic models such as a VAR to describe the
impact of an exogenous shock (innovation) in one variable on the other variables of the
system.
In addition to the impulse response function results, the study will also conduct variance
decomposition analysis. Thus in this study, variance decomposition will shed light on the
proportion of the movement in the, traded values, foreign investor participation and volume
of bonds and stocks traded that are due to their own shocks, versus shocks to other
85
variables. This will help in identifying factors which affect bond and equity market liquidity in
the short-run, medium and long-run.
4.3.4. Granger causality test
Granger (1969) causality is employed to test for the causal relationship between two
variables (assuming two variables). This test states that, if past values of a variable
significantly contribute to forecast the future value of another variable then is said to
Granger-cause . Conversely, if past values of statistically improve the prediction of , then
we can conclude that Granger-causes (Deb and Mukherjee: 2008:3). The test is based
on the general VAR model highlighted above and all the three liquidity variables will be
estimated separately as indicated above.
4.4. Definition of variables and sources of data
The study uses monthly time series data on bonds and equities over the period 2000
January - 2008, September (105 observations). For macroeconomic data (CPIX, Interest
Rates (Repo rate) and JSE All share index the main sources of data is JSE, Bond Exchange
of South Africa‘s (BESA) online publications.
Monthly data spanning the period 2000 September 2008 JSE all share index is collected
from the Johannesburg stock exchange publications using the JSE all-share index including
trading volumes, foreign investor participation and values of trades. In total there are 105
observations in the sample data series in this study. The All Share Index is chosen as it is
considered to be South Africa‘s leading market indicator.
Foreign investor participation (FIP), traded values and volume (VOL) represents the three
measures of liquidity in this study.
Volume traded liquidity measure. Volume is a number that tells you the number of contracts
traded that day i.e. the volume on a stock or bonds indicates how many shares of stock or
bonds have traded. This is calculated as a certain volume, or quantity of shares or bonds,
per time unit and it used to capture the depth dimension of liquidity. There is also a relation
to the time dimension since higher volume leads to a shorter time needed for a certain
amount of shares or bonds to be traded. As in Benić and Franić (2008:481), the values of
volume-related measures should be higher in order to indicate high liquidity. Data from 2000
January to September 2008 was provided by the JSE
86
Foreign investor participation: This measure of liquidity refers to the buying and selling of
financial assets i.e. bonds and equities by foreigners in the domestic financial market.
Foreign investors affect local market liquidity. Market microstructure research emphasizes
the importance of asymmetric information as a determinant of liquidity by arguing that, if
foreign investors are on average better informed than local investors, extensive foreign
presence can be associated with increased adverse selection costs for local traders,
undermining market liquidity and if foreign investors are less well informed, they may act as
liquidity (or ―noise‖) traders that improve market liquidity. It is argued by Vagias and van
Dijkeven (2001:2) that, in the absence of systematic differences in how well foreign and local
investors are informed, the trading behaviour of foreign investors can diminish local market
liquidity to the extent that it is associated with increased order imbalances and/or market
volatility. Foreign investor participation is also used as a liquidity proxy in this study as
foreign investors have effect in liquidity. Data from 2000 January to September 2008 was
provided by the JSE.
Trade Values: The value of shares and bonds traded is the total number of shares or bonds
traded multiplied by their respective matching prices. This liquidity indicator shows the total
value of the quantities bought multiplied by its corresponding price. Data for bonds and
equities trade values is obtained from the Johannesburg Stock Exchange. Data from 2000
January to September 2008 was provided by the JSE
CPI represents inflation as measured by the Consumer Price Index (CPI).. Data from 2000
January to September 2008 is collected from the SARB.
REP represents the repo rate, a tool which is currently used by the South African Reserve
bank in monetary policy. Data from 2000 January to September 2008 is collected from the
SARB.
EX represents the exchange rate. Data from 2000 January to September 2008 is collected
from the SARB.
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CHAPTER 5
ESTIMATION AND INTERPRETATION OF THE RESULTS
5.1. Introduction
This chapter presents the estimations of models for the liquidity linkages between the South
African bond and equity market. To analysing the empirical results, a unit root test, Lag
length selection criteria, cointegration test, Vector error correction model, Correlation matrix,
Impulse response function, Variance decomposition and the Granger Causality test will all
be estimated for each of the three different liquidity measures employed in this study.
5.2. Unit root testing
It is argued that when the dependant and independent variables have unit roots, traditional
estimation methods using observations on levels of those variables will likely find a
statistically significant relationship, even when meaningful economic linkage is absent
(Granger & Newbold 1974). For meaningful policy analysis it is important therefore to
distinguish between a correlation that arises from a shared trend and one associated with an
underlying causal relationship. Thus, in this study to achieve that, the data was subjected to
two types of tests to establish their univariate time series behaviour in order to determine the
basic unit of observation. These tests are the Augmented Dickey-Fuller (ADF) and Phillips-
Perron (PP).
Table 5.1 and 5.2 below provides the summary results of the unit root test ucing the ADF
and PP tests; it also shows the t-statistics and consequently the order of integration of the
variables. All variables are either integrated at level or after first-differencing . The
unit root tests considered both the null hypothesis of a random walk without a drift
(untrended) and a random walk with a drift and trended (trended). The results of these tests
are reported in Tables 5.1 and 5.2
88
Table 5.1 Unit Root Test- ADF (Variables in Levels and First Difference)
Variables In Levels In First-Difference Test Statistic
LVOLE I(0) I(1) -13.2***
LVOLB I(0) I(1) -13.0***
FIPE I(0) -5.8***
FIPB I(0) -9.7***
LTVE I(0) I(1) -9.4***
LTVB I(0) I(1) -13.7***
CIP I(0) I(1) -5.3***
EX I(0) I(1) -7.2***
Repo Rate I(0) I(1) -4.2***
Notes:
i. Where: (*), (**) and (***) indicate 10%, 5% and 1% significance level, respectively ii. The ADF and PP tests are based on the null hypothesis of unit roots
Table 5.2: Unit Root Test Phillips- Perron (Variables in Levels and First Difference)
Variables In Levels In First-Difference Test Statistic
LVOLE I(0) I(1) -3.7***
LVOLB I(0) I(1) -3.1**
FIPE I(0) -6.1***
FIPB I(0) -9.6***
LTVE I(0) I(0) -19.2***
LTVB I(0) -3.6***
CIP I(0) I(1) -5.9***
EX I(0) I(1) -7.1***
Repo Rate I(0) I(1) -8.1***
Notes:
i. Where: (*), (**) and (***) indicate 10%, 5% and 1% significance level, respectively ii. Maximum Bandwidth for the PP test has been decided on the basis of Newey-West (1994)
Applying the ADF and PP tests, all the variables (except for FIPB and FIPE at 1% levels of
significance) were found to be non-stationary at their levels as the t-statistics for each
variable was not greater than the critical t-value as indicated in Table 5.1 and 5.2 above. The
variables were then tested for stationarity at first differences. The results of these tests ate
reported in Table 5.1 and 5.2 as I(0) and I(1). The results confirmed that differencing once
was all that that was required to bring these variables to stationarity at all levels of
significance. Having established the existence of unit root, each model is tested below and
the Lag Length Selection Criteria and the Cointegration test are conducted for each model.
89
5.3. Lag Length Selection Criteria
Model 1 establishes the liquidity linkages between the South African bond and equity
markets using volume of trade (VOLE and VOLB) as a liquidity measure in the two markets.
Having established the existence of unit root above, the next step is the Lag Length
Selection Criteria and the Cointegration test.
Model 2 establishes the liquidity linkages between the South African bond and equity
markets using Trade Values (TV) as a liquidity measure in the two markets. Having
established the existence of unit root in section 5.2 above, the next step is the Lag Length
Selection Criteria and the Cointegration test.
Model 3 establishes the liquidity linkages between the South African bond and equity
markets using Foreign Investor Participation (FIPE and FIPB) as a liquidity measure in the
two markets. Having established the existence of unit root in section 5.2 above, the next step
is the Lag Length Selection Criteria and the Cointegration test.
5.3.1. Lag Length Selection Criteria- Liquidity: Volumes of trade model
The choice of optimal lag length of the variables of interest is imperative in econometric
model estimation, especially in a VAR model. This is important to avoid spurious rejection or
acceptance of estimated results. If there are variables with lag length , for example, it is
necessary to estimate coefficients. The lag length also influences the power of
rejecting hypothesis. For instance, if is too large, degrees of freedom maybe wasted.
Moreover, if the lag length is too small, important lag dependences maybe omitted from the
VAR and if serial correlation is present the estimated coefficients will be inconsistent.
The common information criteria are the Akaike Information Criteria (AIC), Schwarz
Information Criterion (SIC), Hannan-Quinn Information Criterion (HQI), Final prediction error
(FPE) and the Likelihood Ratio test (LR). An optimal lag length suggested by the above
information criteria can be chosen as these criteria may sometimes produce conflicting lag
length choices. However, decision about the lag structure of a VAR model could be based
on the fact that a given criterion produces a white noise residual and conserves degrees of
freedom. Table 5.3.1 presents the selection of an optimal lag length for this study.
90
Table 5.3.1: VAR Lag Order Selection Criteria -Liquidity: Volumes of trade model
Lag LogL LR FPE AIC SC HQ 0 -501.5439 NA 0.023631 10.44420 10.57692 10.49787 1 -46.62707 853.5553 3.34e-06 1.579940 2.376242* 1.901925 2 -4.045393 75.50565 2.34e-06 1.217431 2.677318 1.807738* 3 25.18884 48.82418 2.16e-06* 1.130127 3.253600 1.988756 4 44.25593 29.87834 2.49e-06 1.252455 4.039513 2.379405 5 66.00141 31.83359 2.75e-06 1.319558 4.770202 2.714830 6 100.9184 47.51595* 2.36e-06 1.115084* 5.229313 2.778677 7 123.0329 27.81405 2.68e-06 1.174580 5.952394 3.106494 8 150.3204 31.50725 2.82e-06 1.127414 6.568814 3.327650
Notes:
* indicates lag order selected by the criterion
LR: sequential modified LR test statistic (each test at 5% level)
FPE: Final prediction error
AIC: Akaike information criterion
SC: Schwarz information criterion HQ: Hannan-Quinn information criterion
In this model, the optimal lag length was chosen based on the Akaike Information Criterion
(AIC) which is 6. Even though the Schwarz Information Criterion (SIC) is argued by Gujarat
(2003) to impose a harsher penalty for including an increasingly large number of regressors,
the AIC was chosen in this study.
5.3.2. Lag Length Selection Criteria- Liquidity: Trade Values model
The choice of optimal lag length of the variables of interest is imperative in econometric
model estimation, especially in a VAR model. This is important to avoid spurious rejection or
acceptance of estimated results. If there are variables with lag length , for example, it is
necessary to estimate coefficients. The lag length also influences the power of
rejecting hypothesis. For instance, if is too large, degrees of freedom maybe wasted.
Moreover, if the lag length is too small, important lag dependences maybe omitted from the
VAR and if serial correlation is present the estimated coefficients will be inconsistent.
The common information criteria are the Akaike Information Criteria (AIC), Schwarz
Information Criterion (SIC), Hannan-Quinn Information Criterion (HQI), Final prediction error
(FPE) and the Likelihood Ratio test (LR). An optimal lag length suggested by the above
information criteria can be chosen as these criteria may sometimes produce conflicting lag
length choices. However, decision about the lag structure of a VAR model could be based
on the fact that a given criterion produces a white noise residual and conserves degrees of
freedom. Table 5.4.1 presents the selection of an optimal lag length for this section.
91
Table 5.3.2: VAR Lag Order Selection Criteria- Liquidity: Trade Values model
Lag LogL LR FPE AIC SC HQ
0 -565.5175 NA 0.088378 11.76325 11.89596 11.81691 1 -53.67016 960.3733 3.87e-06 1.725158 2.521460* 2.047144 2 -4.687275 86.85626 2.37e-06 1.230665 2.690553 1.820973* 3 26.45588 52.01228 2.11e-06* 1.104002 3.227475 1.962631 4 46.34850 31.17195 2.39e-06 1.209309 3.996367 2.336259 5 67.01667 30.25649 2.70e-06 1.298625 4.749269 2.693897 6 104.5594 51.08912* 2.19e-06 1.040012 5.154241 2.703604 7 125.8098 26.72727 2.53e-06 1.117324 5.895138 3.049238 8 155.4886 34.26833 2.53e-06 1.020853* 6.462253 3.221089
Notes:
* indicates lag order selected by the criterion
LR: sequential modified LR test statistic (each test at 5% level)
FPE: Final prediction error
AIC: Akaike information criterion
SC: Schwarz information criterion HQ: Hannan-Quinn information criterion
In this model, the optimal lag length was chosen based on the Akaike Information Criterion
(AIC) which is 6. Even though the Schwarz Information Criterion (SIC) is argued by Gujarat
(2003) to impose a harsher penalty for including an increasingly large number of regressors,
the AIC was chosen in this study.
5.3.3. Lag Length Selection Criteria- Liquidity: Foreign Investor Participation model
The choice of optimal lag length of the variables of interest is imperative in econometric
model estimation, especially in a VAR model. This is important to avoid spurious rejection or
acceptance of estimated results. If there are variables with lag length , for example, it is
necessary to estimate coefficients. The lag length also influences the power of
rejecting hypothesis. For instance, if is too large, degrees of freedom maybe wasted.
Moreover, if the lag length is too small, important lag dependences maybe omitted from the
VAR and if serial correlation is present the estimated coefficients will be inconsistent.
The common information criteria are the Akaike Information Criteria (AIC), Schwarz
Information Criterion (SIC), Hannan-Quinn Information Criterion (HQI), Final prediction error
(FPE) and the Likelihood Ratio test (LR). An optimal lag length suggested by the above
information criteria can be chosen as these criteria may sometimes produce conflicting lag
length choices. However, decision about the lag structure of a VAR model could be based
on the fact that a given criterion produces a white noise residual and conserves degrees of
freedom. Table 5.5.1 presents the selection of an optimal lag length for this study.
92
Table 5.3.3: VAR Lag Order Selection Criteria- Liquidity: Foreign Investor
Participation model
Lag LogL LR FPE AIC SC HQ
0 -3798.513 NA 7.88e+27 78.42294 78.55566 78.47661 1 -3379.222 786.7105 2.32e+24 70.29324 71.08954* 70.61522 2 -3340.491 68.67741* 1.76e+24* 70.01013* 71.47001 70.60043* 3 -3323.276 28.75071 2.08e+24 70.17064 72.29412 71.02927 4 -3304.717 29.08282 2.43e+24 70.30344 73.09050 71.43039 5 -3282.411 32.65431 2.65e+24 70.35898 73.80963 71.75425 6 -3258.652 32.33145 2.86e+24 70.38457 74.49880 72.04817 7 -3237.187 26.99719 3.29e+24 70.45746 75.23528 72.38938 8 -3215.604 24.91991 3.89e+24 70.52793 75.96933 72.72816
Notes: * indicates lag order selected by the criterion
LR: sequential modified LR test statistic (each test at 5% level)
FPE: Final prediction error
AIC: Akaike information criterion
SC: Schwarz information criterion HQ: Hannan-Quinn information criterion
In this model, the optimal lag length was chosen based on the Schwarz Information Criterion
(SIC) which is 1. Schwarz Information Criterion (SIC) is chosen as it imposes a harsher
penalty for including an increasingly large number of regressors (Gujarat 2003).
5.4. Johansen Cointegration Test
Having tested for unit root, the study then tests for possible Cointegration among the
variables of interest. The study applied the multivariate Cointegration technique developed
by Johansen and Juselius (1990) to the system variables. The Johansen technique was
chosen as it performs better than single-equation and alternative multivariate methods
(Ibrahim 2000). The results of the Cointegration tests are reported in Table 5.4.1 to 5.4.3
below.
Table 5.4.1: Johansen Cointegration Test results- Liquidity: Volumes of trade model
Hypothesized No. of CE(s)
Trace Statistic
0.05 Critical Value
Max-Eigen Statistic
0.05 Critical Value
None * 81.41474 69.81889 34.92945 33.87687 At most 1 46.48528 47.85613 27.92907 27.58434 At most 2 18.55621 29.79707 12.66656 21.13162 At most 3 5.889652 15.49471 5.742660 14.26460 At most 4 0.146992 3.841466 0.146992 3.841466
Notes:
Trace test indicates 1 cointegrating eqn(s) at the 0.05 level Max-eigenvalue test indicates 2 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values
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From table 5.4.1 above, the null hypothesis of no Cointegration was rejected at 0.05 level of
significance. As indicated that there is 1 cointegrating relationship from the Trace test and 2
cointegrating relationship from the Max-Eigenvalue between the volumes of equity and
bonds traded. This implies an existence of a long-run relationship among the variables in the
study. Thus a Vector Error Correction Model (VECM) can be specified from the results of the
regression analysis.
The signs of all variables in the cointegrating equation are as expected, however the primary
goal of the study is to determine the level of interaction or links among the volumes traded
for both the bond and equity markets, values of trades for both markets and foreign investor
participation in both markets and how all the variables influence each other (see full table in
appendix 1 A). The next step in the study is to estimate a VECM normalized on the variable
of interest (Volume of bonds and equity traded) where each of the variables of interest is a
function of the remaining variables in the VECM.
The long run regression results based on the Vector Error Correction Estimates indicates
that there is a positive relationship between liquidity in the bond and equity markets
measured by the volume traded in the markets. This is also consistent with the work of
Chordia et al (2003), who highlighted that liquidity and volatility shocks are positively related
across markets. The Empirical results also show that all the macroeconomic variables are
significant. CPI is negative and significant and this is consistent with theory as an increase in
inflation results in the Reserve Bank increasing the repo rate to reduce inflationary
pressures. An increase in the repo rate will thus lead to an increase in the risk free rate and
hence a decrease in the bond prices since there is a negative relationship between the bond
prices and yield. This is likely to results in the reduction in the volumes of bond traded in the
secondary markets as bonds will not be lucrative investment and this will have an effect in
the equity markets as well as a shocks in these markets are positively related.
Exchange rate is found to be significant and positive. This again conforms to theory since an
appreciation of the ZAR against say US dollar will mean an increase in bond returns and
dividends. The repo rate is also significant although negatively singed (see full VECM table
in appendix 1B).
94
Table 5.4.2: Error Correction Model Results- Liquidity: Volumes of trade model
Error Correction: D(LVOLE) D(LVOLB) D(CPI) D(REPO_RATE)
D(EX)
CointEq1 -0.314317 -0.224384 0.918846 0.614428 0.261195
(0.10752) (0.12849) (0.38239) (0.27793) (0.27611) [-2.92322] [-1.74628] [ 2.40293] [ 2.21073] [ 0.94598]
Notes:
Standard errors in ( ) & t-statistics in [ ]
Table 5.4.2 above summarises the estimated output for the ECM model. The full output is
available in the appendix. A critical aspect of the ECM is the necessity of the coefficient of
adjustment to have a negative coefficient. This indicates that each period, errors are
continually being reduced and the model is bearing to its long-run structural level. LVOLE
and LVOLB which indicate our liquidity measure (volume) have coefficients that are
negative, indicating that any disequilibrium in these variables doesn‘t take long to adjust.
However, CPI, EX, and Repo Rate have coefficients that are positive indicating that any
disequilibrium in these variables takes long to adjust.
Table 5.4.3: Johansen Cointegration Test results- Liquidity: Trade Values model
Hypothesized No. of CE(s)
Trace Statistic
0.05 Critical Value
Max-Eigen Statistic
0.05 Critical Value
None * 99.60539 69.81889 39.01868 33.87687 At most 1 60.58670 47.85613 25.69935 27.58434 At most 2 34.88735 29.79707 19.93566 21.13162 At most 3 14.95169 15.49471 10.55068 14.26460 At most 4 4.401008 3.841466 4.401008 3.841466
Notes: Trace test indicates 3 cointegrating eqn(s) at the 0.05 level Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values
From table 5.4.3 above, the null hypothesis of no Cointegration was rejected at 0.05 level of
significance. As indicated that there are 3 cointegrating relationship from the Trace test and
1 cointegrating relationship from the Max-Eigenvalue between the values of equity and
bonds traded. This implies an existence of a long-run relationship among the variables in the
study. Thus a Vector Error Correction Model (VECM) can be specified from the results of the
regression analysis.
The signs of all variables in the cointegrating equation are as expected, however the primary
goal of this section is to determine the level of interaction or links among the trade values for
95
both the bond and equity markets (see full table in appendix 2 A). The next step in the study
is to estimate a VECM normalized on the variable of interest (trade values (TV) of bonds and
equity traded) where each of the variables of interest is a function of the remaining variables
in the VECM.
The long run regression results based on the Vector Error Correction Estimates indicates
that there is a negative relationship between liquidity in the bond and equity markets
measured by the trade values in the markets. The Empirical results also show that all the
macroeconomic variables are significant. CPI is negative and significant and this is
consistent with theory as an increase in inflation results in the Reserve Bank increasing the
repo rate to reduce inflationary pressures. An increase in the repo rate will thus lead to an
increase in the risk free rate and hence a decrease in the bond prices since there is a
negative relationship between the bond prices and yield. This is likely to results in the
reduction in trade values for bond traded in the secondary markets as bonds will not be
lucrative investment and this will have an effect in the equity markets as well as a shocks in
these markets are positively related as also indicated in model 1.
Exchange rate is found to be significant and positive. This again conforms to theory since an
appreciation of the ZAR against say US dollar will mean an increase in bond returns and
dividends. The repo rate is also significant although positively singed. The full table of the
VECM is presented on the appendix 2 (B).
Table 5.4.4: Error Correction Model Results- Liquidity: Trade Values model
Error Correction: D(LTVE) D(LTVB) D(CPI) D(REPO_RATE)
D(EX)
CointEq1 -0.169552 0.133414 0.179899 -0.159538 -0.410531
(0.06547) (0.06884) (0.22905) (0.14889) (0.13334) [-2.58987] [ 1.93806] [ 0.78542] [-1.07152] [-3.07879]
Notes:
Standard errors in ( ) & t-statistics in [ ]
Table 5.4.3 above summarises the estimated output for the ECM model. The full output is
available in the appendix 2 (C), critical aspect of the ECM is the necessity of the coefficient
of adjustment to have a negative coefficient. This indicates that each period, errors are
continually being reduced and the model is bearing to its long-run structural level. Trade
values of stock (LTVE), Repo Rate and exchange rate (EX) have coefficients that are
negative, indicating that any disequilibrium in these variables doesn‘t take long to adjust.
96
However, CPI, and trade values for bonds have coefficients that are positive indicating that
any disequilibrium in these variables takes long to adjust.
Table 5.4.5: Johansen Cointegration Test results- Liquidity: Foreign Investor
participation model
Hypothesized No. of CE(s)
Trace Statistic
0.05 Critical Value
Max-Eigen Statistic
0.05 Critical Value
None * 109.9001 69.81889 57.03316 33.87687 At most 1 52.86698 47.85613 32.69902 27.58434 At most 2 20.16796 29.79707 14.46144 21.13162 At most 3 5.706519 15.49471 3.622996 14.26460 At most 4 2.083523 3.841466 2.083523 3.841466
Notes: Trace test indicates 2 cointegrating eqn(s) at the 0.05 level Max-eigenvalue test indicates 2 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values
From table 5.4.5 above, the null hypothesis of no Cointegration was rejected at 0.05 level of
significance. As indicated that there are 2 cointegrating relationship from both the Trace test
and the Max-Eigenvalue between foreign investor participation in equity and bond markets
respectively. This implies an existence of a long-run relationship among the variables in the
study. Thus a Vector Error Correction Model (VECM) can be specified from the results of the
regression analysis.
The signs of all variables in the cointegrating equation are as expected, however the primary
goal of this section is to determine the level of interaction or links among the foreign investor
participation (FIPB and FIPE) for both the bond and equity markets (see full table in
appendix 3 A). The next step in the study is to estimate a VECM normalized on the variable
of interest (foreign investor participation (FIP) of bonds and equity traded) where each of the
variables of interest is a function of the remaining variables in the VECM.
The long run regression results based on the Vector Error Correction Estimates indicates
that there is a negative relationship between liquidity in the bond and equity markets
measured by the foreign investor participation in the markets. The Empirical results also
show that all the macroeconomic variables (with the exception of exchange rate) are
significant. CPI is negative and significant and this is consistent with theory as an increase in
inflation results in the Reserve Bank increasing the repo rate to reduce inflationary
pressures. An increase in the repo rate will thus lead to an increase in the risk free rate and
hence a decrease in the bond prices since there is a negative relationship between the bond
97
prices and yield. This is likely to results in the reduction in foreign investor participation in the
in the secondary markets as bonds will not be lucrative investment.
Exchange rate is found to be significant and positive. This again conforms to theory since an
appreciation of the ZAR against say US dollar will mean an increase in bond returns and
dividends. The repo rate is also insignificant although positively singed. The full table of the
VECM is presented on the appendix 3 (B).
Table 5.4.6: Error Correction Model Results- Liquidity: Foreign Investor participation
model
Error Correction: D(FIPB) D(FIPE) D(CPI) D(EX) D(REPO_RATE)
CointEq1 -1.217566 -208430.0 1.03E-05 -1.17E-05 -4.31E-07
(0.18315) (145343.) (2.1E-05) (1.3E-05) (1.4E-05) [-6.64788] [-1.43405] [ 0.48783] [-0.93522] [-0.03044]
Notes:
Standard errors in ( ) & t-statistics in [ ]
Table 5.4.6 above summarises the estimated output for the ECM model. The full output is
available in the appendix 3 B. A critical aspect of the ECM is the necessity of the coefficient
of adjustment to have a negative coefficient. This indicates that each period, errors are
continually being reduced and the model is bearing to its long-run structural level. Foreign
Investor Participation in both the bond and equity markets (FIPB and FIPE), Repo Rate and
exchange rate (EX) have coefficients that are negative, indicating that any disequilibrium in
these variables doesn‘t take long to adjust. However, CPI has a coefficient that is positive
indicating that any disequilibrium in this variable takes long to adjust.
5.5. Correlation matrixes
The short-term relationship between variables can also be illustrated by means of a
correlation matrix. The correlation matrixes for the variables of interest are highlighted below.
Table 5.5.1: Correlation Matrix- Liquidity: Volumes of trade model
Variable LVOLE LVOLB CPI REPO_RATE EX
LVOLE 1 LVOLB 0.54 1 CPI 0.19 0.00 1 REPO_RATE 0.05 -0.02 0.24 1 EX 0.15 0.29 -0.33 -0.11 1
98
Table 5.5.1 above presents the contemporaneous relations between innovations in the
variables. From the empirical results, it is evident that innovations in liquidity in the bond and
equity markets are positively related, this is also consistent with theoretical expectations and
the work of Chordia et al (2001 & 2003) who argued that liquidity in bond and equity markets
are co-determined and volumes of trade in one market affect volumes of trade in the other
market. Repo rate is positively related to innovation in the equity markets but negatively
related to innovations in the bond market. This negative relationship between the repo rate
and the bond market liquidity measured by volumes of bonds traded can be best interpreted
with reference to institutional investors who are sensitive to changes in the interbank rate,
which determines the return on short term investments. Many of these investors are mutual
funds and investment companies who must invest their limited funds for best use. When the
repo rate increases, short term investments become more attractive than bonds and these
institutional investors will then invest in short term investments than bonds. The Exchange
rate and inflation is positively related to liquidity both in the bond and equity markets (see full
table in appendix 1 B).
Table 5.5.2: Correlation Matrix- Liquidity: Trade Values model
Variable LTVE LTVB CPI EX REPO_RATE
LTVE 1 LTVB 0.51 1 CPI 0.08 -0.07 1 EX 0.13 0.27 -0.12 1 REPO_RATE -0.01 -0.11 0.31 0.02 1
Table 5.5.2 above presents the contemporaneous relations between innovations in the
variables. From the empirical results, it is evident that innovations in liquidity in the bond and
equity markets measured by trade values are positively related, this is also consistent with
theoretical expectations and the work of Chordia et al (2001 & 2003) who argued that
liquidity in bond and equity markets are co-determined and volumes of trade in one market
affect volumes of trade in the other market. Repo rate is negatively related to innovation in
the equity and bond market liquidities. The negative relationship between the repo rate and
the bond market liquidity measured by volumes of bonds traded can be best interpreted with
reference to institutional investors who are sensitive to changes in the interbank rate, which
determines the return on short term investments. Many of these investors are mutual funds
and investment companies who must invest their limited funds for best use. When the repo
rate increases, short term investments become more attractive than bonds and these
institutional investors will then invest in short term investments than bonds. The Exchange
rate is positively related to liquidity in both markets whilst inflation is positively related to
99
liquidity in the equity markets and negatively related to liquidity in the equity markets (see full
table in appendix 2 B).
Table 5.5.3: Correlation Matrix- Liquidity: Foreign Investor Participation model
Variable FIPE FIPB CPI EX REPO_RATE
FIPE 1 FIPB 0.06 1 CPI -0.15 0.02 1 EX 0.06 -0.24 -0.13 1 REPO_RATE 0.03 0.07 0.35 -0.01 1
Table 5.5.3 above presents the contemporaneous relations between innovations in the
variables. From the empirical results, it is evident that innovations in liquidity in the bond and
equity markets measured by foreign are positively related, this is also consistent with
theoretical expectations and the work of Chordia et al (2001 & 2003) who argued that
liquidity in bond and equity markets are co-determined. CPI is positively related to foreign
investor participation in bonds whilst it is negatively related to foreign investor participation in
equities. Repo rate is positively related to innovation in the equity and bond market
liquidities. The exchange rate is positively related to innovations in the equity markets and
negatively related to innovations in bonds (see full table in appendix 3 D).
5.6. Impulse Response Function
The impulse response functions indicate the dynamic response of each variable to a one-
period standard deviation shock to the innovations of each variable. The interpretation of the
impulse response function does take into account the use of the first differencing of the
variables as well as the vector error correction estimates. Thus, a one-time shock to the first
difference in a variable is a permanent shock to the level of that variable.
5.6.1. Impulse Response Function- Liquidity: Volumes of trade model
Impulse response function allows issues to be addressed concerning the effects of market
microstructure and macroeconomic variables on bond and equity market liquidity in the
South African markets. Of particular interest in this section are the dynamic responses of
volumes (lvole and lvolb) of bonds and equities traded, in South Africa to themselves and to
innovations in each microstructure and macroeconomic variable. Figure 5.6.1 below
illustrates the response of volumes traded to a unit standard deviation change in a particular
variable, traced forward over a period of 36 months. In the figures, month 1-36 plots effect
100
from +1 to +36 months. The focus of the analysis is only on the variables of interest in this
study, in this case the volumes of equities and bonds traded.
In the first panel liquidity in the bond markets measured by volumes traded (LVOLB) in the
bond market declines in the first period due to its own shock, slightly increasing in the
second period and have small up and down changes throughout the entire period of 36
months. The empirical results also indicates that the response of market liquidity in the bond
markets due shock from the equity markets positive and there is evidence on an increase in
the first and the second period and minimal thereafter. There is an upward shift from liquidity
in bond markets due to shocks from inflation in the first period and this movement remains
constant for the entire period. Shock from Repo rate and exchange rate have minimal
positive impact on the volumes of bonds traded.
The second panel indicates liquidity in the equity markets measured by the volumes of
equity traded (LVOLE). Due to shock from the bond markets, there is a decline in the first
period and the market minimal ups and down in the second period and there are marginal
decrease and increases thereafter. Due to own shocks, liquidity in the equity market
measured by volumes declines in the first period, slightly increase in the second period with
marginal increase and decreases over the remainder of the period. There is also minimal
impact in equity market liquidity measured by volumes of trade due to shocks from CPI, repo
rate and the exchange rate, though it is positive for both the CPI, EX and slightly negative for
repo-rate.
101
Figure 5.6.1: Impulse Response Function: Volumes of trade model
-.05
.00
.05
.10
.15
.20
5 10 15 20 25 30 35
Response of LVOLB to LVOLB
-.05
.00
.05
.10
.15
.20
5 10 15 20 25 30 35
Response of LVOLB to LVOLE
-.05
.00
.05
.10
.15
.20
5 10 15 20 25 30 35
Response of LVOLB to CPI
-.05
.00
.05
.10
.15
.20
5 10 15 20 25 30 35
Response of LVOLB to EX
-.05
.00
.05
.10
.15
.20
5 10 15 20 25 30 35
Response of LVOLB to REPO_RATE
-.04
.00
.04
.08
.12
5 10 15 20 25 30 35
Response of LVOLE to LVOLB
-.04
.00
.04
.08
.12
5 10 15 20 25 30 35
Response of LVOLE to LVOLE
-.04
.00
.04
.08
.12
5 10 15 20 25 30 35
Response of LVOLE to CPI
-.04
.00
.04
.08
.12
5 10 15 20 25 30 35
Response of LVOLE to EX
-.04
.00
.04
.08
.12
5 10 15 20 25 30 35
Response of LVOLE to REPO_RATE
Response to Cholesky One S.D. Innovations
102
5.6.2. Impulse Response Function- Liquidity: Trade Values model
This allows issues to be addressed concerning the effects of market microstructure and
macroeconomic variables on bond and equity market liquidity in the South African markets.
Of particular interest in this section are the dynamic responses of trade values (LTVE and
LTVB) of equities and bonds traded, in South Africa to themselves and to innovations in
each microstructure and macroeconomic variable. Figure 5.6.2 below illustrates the
response of trade values to a unit standard deviation change in a particular variable, traced
forward over a period of 36 months. In the figures, month 1-36 plots effect from +1 to +36
months. The focus of the analysis is only on the variables of interest in this study, in this
case the trade values of equities and bonds traded.
The first panel indicates liquidity in the bond markets measured by the trade values of bonds
traded (LTVB). Due to own shocks, liquidity in the bond market measured by trade values
declines in the first period and slightly increase in the second period and thereafter having
minimal up and downs. Due to shock from equities, there is a slight increase in the first
period and the market slightly moves up and down the entire period. There is also minimal
impact in bond market liquidity measured by volumes of trade due to shocks from CPI, repo
rate and the exchange rate.
The second panel indicates that liquidity in the equity markets measured by trade values in
the equity markets (LTVE) declines in the first period due to shocks from the bond markets,
increasing in the second period and then have slight ups and down for the remainder of the
period. The empirical results also indicates that, the response of market liquidity in the equity
markets due own shock is negative as liquidity declines in the first period and have minimal
ups and down for the entire period and the market never revert back to equilibrium. Shock
from Repo rate and exchange rate and CPI have minimal impact in liquidity in the equity
markets measured by trade values.
103
Figure 5.6.2: Impulse Response Function: Trade Values model
-.05
.00
.05
.10
.15
.20
5 10 15 20 25 30 35
Response of LTVB to LTVB
-.05
.00
.05
.10
.15
.20
5 10 15 20 25 30 35
Response of LTVB to LTVE
-.05
.00
.05
.10
.15
.20
5 10 15 20 25 30 35
Response of LTVB to CPI
-.05
.00
.05
.10
.15
.20
5 10 15 20 25 30 35
Response of LTVB to EX
-.05
.00
.05
.10
.15
.20
5 10 15 20 25 30 35
Response of LTVB to REPO_RATE
-.05
.00
.05
.10
.15
5 10 15 20 25 30 35
Response of LTVE to LTVB
-.05
.00
.05
.10
.15
5 10 15 20 25 30 35
Response of LTVE to LTVE
-.05
.00
.05
.10
.15
5 10 15 20 25 30 35
Response of LTVE to CPI
-.05
.00
.05
.10
.15
5 10 15 20 25 30 35
Response of LTVE to EX
-.05
.00
.05
.10
.15
5 10 15 20 25 30 35
Response of LTVE to REPO_RATE
Response to Cholesky One S.D. Innovations
104
5.6.3. Impulse Response Function- Liquidity: Foreign Investor Participation model
Impulse response allows issues to be addressed concerning the effects of market
microstructure and macroeconomic variables on bond and equity market liquidity in the
South African markets. Of particular interest in this section are the dynamic responses of
foreign investor participation (FIPE and FIPB) in both the equities and bonds, in South Africa
to themselves and to innovations in each microstructure and macroeconomic variable.
Figure 5.5.1 below illustrates the response of foreign investor participation to a unit standard
deviation change in a particular variable, traced forward over a period of 36 months. In the
figures, month 1-36 plots effect from +1 to +36 months. The focus of the analysis is only on
the variables of interest in this study, in this case the trade values of equities and bonds
traded.
The first panel indicates liquidity in the bond markets measured by the foreign investor
participation in bonds (FIPB). Due to own shocks, there is decline in the first period, liquidity
stabilise in the second period and remain constant for the entire period. There is also
minimal impact in bond market liquidity measured by foreign investor participation due to
shocks from CPI, repo rate and the exchange rate and there is positive effect due to shocks
from the equity markets in the first period and remain constant thereafter.
The second indicates that liquidity in the equity markets measured by foreign investor
participation in the equity markets (FIPE) declines in the first period due to its own shocks
and remains constant for the entire period. The empirical results also indicates that the
response of market liquidity in the equity markets due shock from the bond market is
minimal. There is a minimal shift from liquidity in equity markets due to shocks from inflation.
Shock from Repo rate affects the equity markets slightly negative and exchange rate seems
to slightly liquidity in the equity markets measured by foreign investor participation in the first
period and remain constant thereafter.
105
Figure 5.6.3: Impulse Response Function: Foreign Investor Participation model
-1,000
0
1,000
2,000
3,000
4,000
5,000
5 10 15 20 25 30 35
Response of FIPB to FIPB
-1,000
0
1,000
2,000
3,000
4,000
5,000
5 10 15 20 25 30 35
Response of FIPB to FIPE
-1,000
0
1,000
2,000
3,000
4,000
5,000
5 10 15 20 25 30 35
Response of FIPB to CPI
-1,000
0
1,000
2,000
3,000
4,000
5,000
5 10 15 20 25 30 35
Response of FIPB to EX
-1,000
0
1,000
2,000
3,000
4,000
5,000
5 10 15 20 25 30 35
Response of FIPB to REPO_RATE
-1,000,000,000
0
1,000,000,000
2,000,000,000
3,000,000,000
4,000,000,000
5 10 15 20 25 30 35
Response of FIPE to FIPB
-1,000,000,000
0
1,000,000,000
2,000,000,000
3,000,000,000
4,000,000,000
5 10 15 20 25 30 35
Response of FIPE to FIPE
-1,000,000,000
0
1,000,000,000
2,000,000,000
3,000,000,000
4,000,000,000
5 10 15 20 25 30 35
Response of FIPE to CPI
-1,000,000,000
0
1,000,000,000
2,000,000,000
3,000,000,000
4,000,000,000
5 10 15 20 25 30 35
Response of FIPE to EX
-1,000,000,000
0
1,000,000,000
2,000,000,000
3,000,000,000
4,000,000,000
5 10 15 20 25 30 35
Response of FIPE to REPO_RATE
Response to Cholesky One S.D. Innovations
106
5.7. Variance Decomposition
―Variance decompositions‖ give the proportion of the movement in the dependent variables
that are due to their own shocks, versus shocks to the other variables. A shock to the
variable will directly affect that variable and will be transmitted to all of the other variables in
the system through the dynamic structure of the VAR‖ (Brooks, 2008:300).
5.7.1. Variance Decomposition- Liquidity: Volumes of trade model
Table 5.7.1 below illustrates the variance decomposition of the volumes of bonds traded
(lvolb) and volumes of stock or equities (lvole). These are the variables of interest in this
section over 36 month horizon using the Choleski decomposition method in order to identify
the most effective instruments in targeting each of the variable of interest. This helps in
separating innovations of the endogenous variables into proportions that can be attributed to
their own innovations and innovations from other variables.
Table 5.7.1: Variance Decomposition Results- Liquidity: volumes of trade model
Variance Decomposition
PERIOD S.E. LVOLE LVOLB CPI REPO_RATE
EX
LVOLE
1 36
0.1 0.4
100 58.4
0.0 8.2
0.0 24.9
0.0 2.2
0.0 6.2
LVOLB 4
36 0.2 0.6
17.7 27.2
78.7 57.3
1.0 3.1
1.8 3.9
0.8 8.4
CPI 26 1
6.2 0.5
62.4 3.7
1.5 1.4
9.4 94.8
2.5 0.0
24.3 0.0
REPO_RATE 2
28 0.5 3.4
0.1 42.4
0.4 1.2
2.3 3.5
96.8 32.5
0.3 20.4
EX 1
14 0.3 1.9
2.1 15.7
6.4 18.5
11.1 7.8
0.1 1.9
80.4 56.2
The first panel in table 5.7.1 above indicates that the predominant sources of variations in
the volumes of equities (LVOLE) forecast errors is own shocks, which account for between
54 per cent and 100 per cent of the forecast errors in volumes of equities over a 36 months
horizon. Volume of trade in the bond market (LVOLB), inflation rate (CPI), repo rate (REPO_
RATE) and exchange rate (EX) are also important as a source of forecast variance in equity
volumes.
Panel B, indicates that the predominant sources of variations in the volumes of bonds
(LVOLB) forecast errors is own shocks, which account for between 57 per cent and 79 per
cent of the forecast errors in the volumes of bonds over a 36 months period. Volume of trade
107
in the equity market (LVOLE), inflation rate (CPI), repo rate (REPO_ RATE) and exchange
rate (EX) are also important as a source of forecast variance in equity volumes.
Panel C, indicates that the predominant sources of variations in CPI forecast errors is own
shocks which account for between 9 per cent and 95 per cent. Panel D, indicates that the
predominant sources of variations in the repo rate (REPO_RATE) forecast errors are own
shocks, which account for between 33 per cent and 97 per cent. The last pane indicates that
the predominant sources variations in the EX forecast errors are own shocks which account
for between 56 per cent and 80 per cent.
The result from the variance decomposition indicates that own shocks explain a greater part
of the variability of bonds and stock market liquidities measured by traded volumes (see full
table in appendix 1 D).
5.7.2. Variance Decomposition- Liquidity: Trade Values model
Table 5.7.2 below illustrates the variance decomposition of the trade values for bonds and
trade values for equities (LTVB and LTVE). These are the variables of interest in this section
over 36 month horizon using the Choleski decomposition method in order to identify the
most effective instruments in targeting each of the variable of interest. This helps in
separating innovations of the endogenous variables into proportions that can be attributed to
their own innovations and innovations from other variables.
Table 5.7.2: Variance Decomposition Results: Trade Values model
Variance Decomposition
PERIOD S.E. LTVE LTVB CPI EX REPORATE
LTVE
1 36
0.2 0.5
100 53.9
0.0 2.1
0.0 2.6
0.0 15.4
0.0 26.4
LTVB 1
36 0.2 0.3
25.7 49.1
74.3 35.9
0.0 3.9
0.0 6.5
0.0 4.6
CPI 1
36 0.5 3.9
0.6 30.0
1.6 8.8
97.8 27.9
0.0 23.2
0.0 10.0
EX 1
36 0.3 1.5
1.8 2.3
5.8 28.1
0.8 5.5
91.7 58.4
0.0 5.7
REPO_RATE 1
36 0.3 2.6
0.0 16.3
1.5 10.7
9.1 15.0
0.7 34.1
88.6 23.9
The first panel in table 5.7.2 above indicates that the predominant sources of variations in
the trade values for equities (LTVE) forecast errors is own shocks, which account for
between 54 per cent and 100 per cent of the forecast errors in trade values of equities over a
108
36 months horizon. Inflation rate (CPI), repo rate (REPO_ RATE) and exchange rate (EX)
are also important as a source of forecast variance in equity volumes.
Panel B, indicates that the predominant sources of variations in the trade values for bonds
(LTVB) forecast errors is own shocks, which account for between 36 per cent and 74 per
cent of the forecast errors in the trade values of bonds over a 36 months period. Trade
values for equities (LTVE), inflation rate (CPI), repo rate (REPO_ RATE) and exchange rate
(EX) are also important as a source of forecast variance in equity volumes.
Panel C, indicates that the predominant sources of variations in CPI forecast errors is own
shocks which account for between 28 per cent and 98 per cent. Panel D, indicates
predominant sources variations in the EX forecast errors are own shocks which account for
between 58 per cent and 92 per cent. The last panel indicates that the predominant sources
of variations in the repo rate (REPO_RATE) forecast errors are own shocks, which account
for between 24 per cent and 89 per cent.
The result from the variance decomposition indicates that own shocks explain a greater part
of the variability of bonds and stock market liquidities measured by trade values (see full
table in appendix 2 D).
5.7.3. Variance Decomposition- Liquidity: Foreign Investor Participation model
Table 5.7.3 below illustrates the variance decomposition of foreign investor participation in
both the bond and equity markets (FIPB and FIPE). These are the variables of interest in this
section over 36 month horizon using the Choleski decomposition method in order to identify
the most effective instruments in targeting each of the variable of interest. This helps in
separating innovations of the endogenous variables into proportions that can be attributed to
their own innovations and innovations from other variables.
109
Table 5.7.3: Variance Decomposition Results: Foreign Investor Participation model
Variance Decomposition
PERIOD S.E. FIPE FIPB CPI EX REPORATE
FIPE
1 36
3.6 4.6
100 68.9
0.0 2.5
0.0 5.9
0.0 14.7
0.0 7.7
FIPB 1
36 4852 5119
0.4 4.1
99.6 90.9
0.0 1.1
0.0 1.7
0.0 2.2
CPI 1
36 0.5 3.9
2.1 4.9
0.1 0.3
97.8 57.9
0.0 28.1
0.0 8.8
EX 1
36 0.3 1.4
0.3 2.3
5.8 8.2
1.2 0.5
92.5 86.9
0.0 2.1
REPO_RATE 1
36 0.3 2.4
0.1 2.1
0.4 1.1
12.9 22.5
0.2 49.1
86.3 25.1
The first panel in table 5.7.3 above indicates that the predominant sources of variations in
the foreign investor participation in equities (FIPE) forecast errors is own shocks, which
account for between 69 per cent and 100 per cent of the forecast errors in foreign investor
participation in equities over a 36 months horizon. Inflation rate (CPI), repo rate (REPO_
RATE) and exchange rate (EX) and foreign investor participation in bonds (FIPB) are also
important as a source of forecast variance in equity volumes.
Panel B, indicates that the predominant sources of variations in foreign investor participation
in bonds (FIPB) forecast errors is own shocks, which account for between 91 per cent and
97 per cent of the forecast errors in foreign investor participation of bonds over a 36 months
period. Foreign investor participation in equities (FIPE), inflation rate (CPI), repo rate
(REPO_ RATE) and exchange rate (EX) are also important as a source of forecast variance
in equity volumes.
Panel C, indicates that the predominant sources of variations in CPI forecast errors is own
shocks which account for between 58 per cent and 98 per cent. Panel D, indicates
predominant sources variations in the EX forecast errors are own shocks which account for
between 87 per cent and 93 per cent. The last panel indicates that the predominant sources
of variations in the repo rate (REPO_RATE) forecast errors are own shocks, which account
for between 25 per cent and 86 per cent.
The result from the variance decomposition indicates that own shocks explain a greater part
of the variability of bonds and stock market liquidities measured by trade values (see full
table in appendix 3D).
110
5.8. Diagnostic Checks
The VAR model was subjected to rigorous diagnostic tests. Diagnostic checks are crucial in
this analysis because if there is a problem in the residuals from the estimation of the model,
it will be an indication that the model is not efficient such that parameter estimates from such
a model may be biased. The VAR was tested for AR Roots test and serial correlation and
the results are indicated in figure 5.8.1 to 5.8.3
Figure 5.8.1 AR Roots Graph- Liquidity: Volumes of trade model
The AR Roots Graph reports the inverse roots of the characteristic AR polynomial. The
estimated VAR is stable (stationary) if all roots have modulus less than one and lie inside the
unit circle. If the VAR is not stable, certain results such as impulse response standard errors
are not valid and cannot be relied upon. Figure 5.8.1 above shows that all roots lie inside the
unit circle which is an indication that our VAR is stable.
The model was tested for serial correlation and the results (p-value of 0.2034) indicate that
there is no serial correlation in the variables (see Appendix 1 E).
As for the normality test, the results fail to reject the hypothesis of normal distribution as the
JB test of 175.4212 and a p value of 0.000 is a clear indication of normality at 1 per cent and
5 per cent significance level. At 10 per cent level, the hypothesis of normality caused by the
outliers is rejected (Appendix 1 F).
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
Inverse Roots of AR Characteristic Polynomial
111
The result of the White Heteroskedasticity (no cross terms) p value of 0.3135 implies the null
of homoscedastic residuals cannot be rejected and there is no indication of
Heteroskedasticity (Appendix 1 G).
Figure 5.8.2 AR Roots Graph- Liquidity: Trade values model
The AR Roots Graph reports the inverse roots of the characteristic AR polynomial. The
estimated VAR is stable (stationary) if all roots have modulus less than one and lie inside the
unit circle. If the VAR is not stable, certain results such as impulse response standard errors
are not valid and cannot be relied upon. Figure 5.8.2 above shows that all roots lie inside the
unit circle which is an indication that our VAR is stable.
The model was tested for serial correlation and the results (p-value of 0.0008 indicates that
there is no serial correlation in the variables (see Appendix 2 E).
As for the normality test, the results fail to reject the hypothesis of normal distribution as the
JB test of 206.9296 and a p value of 0.000 is a clear indication of normality at 1 per cent and
5 per cent significance level. At 10 per cent level, the hypothesis of normality caused by the
outliers is rejected (Appendix 2 F).
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
Inverse Roots of AR Characteristic Polynomial
112
The result of the White Heteroskedasticity (no cross terms) p value of 0.0002 implies the null
of homoscedastic residuals cannot be rejected and there is no indication of
Heteroskedasticity (Appendix 2 G).
Figure 5.8.3 AR Roots Graph- Liquidity: Foreign Investor Participation model
The AR Roots Graph reports the inverse roots of the characteristic AR polynomial. The
estimated VAR is stable (stationary) if all roots have modulus less than one and lie inside the
unit circle. If the VAR is not stable, certain results such as impulse response standard errors
are not valid and cannot be relied upon. Figure 5.8.3 above shows that all roots lie inside the
unit circle which is an indication that our VAR is stable.
The model was tested for serial correlation and the results (p-value of 0.1015 indicates that
there is no serial correlation in the variables (see Appendix 3E).
As for the normality test, the results fail to reject the hypothesis of normal distribution as the
JB test of 319.9915 and a p value of 0.000 is a clear indication of normality at 1 per cent and
5 per cent significance level. At 10 per cent level, the hypothesis of normality caused by the
outliers is rejected (Appendix 3F).
The result of the White Heteroskedasticity (no cross terms) p value of 0.0927 implies the null
of homoscedastic residuals cannot be rejected and there is no indication of
Heteroskedasticity (Appendix 3G).
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
Inverse Roots of AR Characteristic Polynomial
113
5.9. Granger Causality Test
To test for the casual relationship between the variables of interest in this section (volumes
traded) for both the bond and equity markets, Granger (1969) causality is employed. It has to
be highlighted that finding causality between the variables does not necessarily mean that a
movement in one of the variables causes movements in the other variables. However, it
simply means the chronological ordering of movements in the series. Table 5.9.1 to 5.9.3
below shows the results from the Granger Causality Test with the full table of the results
shown in (Appendix 1 H, 2 H and 3H).
Table 5.9.1: Granger Causality Test results- Liquidity: Volumes of trade model
Null hypothesis: LVOLE does not Granger-cause LVOLB, and
Null hypothesis: LVOLB does not Granger-cause LVOLE
Test Significance Level
LVOLE (0.0154) 15.71183
LVOLB (0.2165) 8.305785
Notes:
i. Chi-square statistics and P-values (in parentheses) from Granger causality tests
The empirical results from the Granger Causality test in table 5.9.1 above shows some
evidence of uni-directional causality between the two variables of interest in this section.
Liquidity measured by volumes of trade (LVOLE) in the equity market granger causes
liquidity in the bond market measured by the volumes of trade in the in the bond markets
(LVOLB) at 5 per cent significance level. This indicates that there is some form of liquidity
interaction between the two markets although it is uni-direction when liquidity is measured by
the volumes of trade in both markets and there is no evidence of liquidity moving from the
bond to equity markets when trade volumes is used as a liquidity measure.
Table 5.9.2: Granger Causality Test results- Liquidity: Trade values model
Null hypothesis: LTVE does not Granger-cause LTVB, and
Null hypothesis: LTVB does not Granger-cause LTVE
Test Significance Level
LTVE (0.0115) 19.71070
LTVB (0.1721) 11.55715
Notes: Chi-square statistics and P-values (in parentheses) from Granger causality tests
114
The empirical results from the Granger Causality test in table 5.9.2 above shows some
evidence of uni-directional causality between the two variables of interest of interest in this
section. Liquidity measured by trade values (LTVE) in the equity markets granger causes
liquidity in the bond market measured trade values (LTVB) at 5 per cent significance level.
However, there is no evidence of liquidity flow from the bond to equity markets and again as
when liquidity was measured by volumes of trade, the evidence of liquidity interactions runs
from the equity to bond markets.
To test for the casual relationship between the variables of interest in this section (foreign
investor participation (FIPB and FIPE) for both the bond and equity markets, Granger (1969)
causality is employed.
Table 5.9.3: Granger Causality Test- Liquidity: Foreign Investor Participation model
Null hypothesis: FIPE does not Granger-cause FIPB, and
Null hypothesis: FIPB does not Granger-cause FIPE
Test Significance Level
FIPB Granger causes FIPE (0.0011) 10.69201
FIPE Granger causes FIPB (0.0010) 10.77905
Notes:
i. Chi-square statistics and P-values (in parentheses) from Granger causality tests
The empirical results from the Granger Causality test in Table 5.9.3 above indicates strong
evidence of causality between the two variables of interest of interest in this section. Liquidity
in the equity market measured foreign investor participation in bonds (FIPE) granger causes
liquidity in the bond market measured by foreign investor participation in bond markets
(FIPB) at 1 per cent significance level. There is also evidence of causality between liquidity
in bond and equity markets as there is evidence of bi-directional causality between liquidities
in the two markets both at 1 per cent significance level.
5.10. Summary- Liquidity: Volumes of trade model, Trade values model and Foreign
Investor Participation model
Model 1 focuses on interpreting the result of the model specified in chapter 4. The
cointegration and VECM were first conducted to determine the long term relationship
between the volumes of bonds and equities traded. Cointegration was established resulting
in the VECM being specified. All the variables were significant with the exception of
115
exchange rate. Impulse response and Variance decomposition functions were also
constructed to trace the temporal and directional response of volumes traded in equities and
bonds, to structural innovations in the macroeconomic variables and as well as tracing the
movements in a sequence of the variables to their own shocks versus shocks to the other
variables.
The impulse response indicates that due shocks from the equity market; there is positive
impact to liquidity in the bond markets when volumes traded are used as liquidity measure.
There was evidence of decline in liquidity in equity markets due to shocks from bond
markets when volumes of trade are used as liquidity measure. Evidence of uni-directional
causality was observed running from equities to bonds at 5 per cent significance level when
volumes of trade is employed as a liquidity measure.
Model 2 focuses on interpreting the result of the model specified in chapter 4. The
cointegration and VECM were first conducted to determine the long term relationship
between the trade values (TVB and TVE) of both the bonds and equities markets.
Cointegration was established resulting in the VECM being specified. All the variables were
significant with the exception of CPI. Impulse response and Variance decomposition
functions were also constructed to trace the temporal and directional response of trade
values in equities and bonds, to structural innovations in the macroeconomic variables and
as well as tracing the movements in a sequence of the variables to their own shocks versus
shocks to the other variables.
The impulse response function also indicated that there is positive impact on liquidity in the
bond market due to liquidity shocks from the equity markets. However there is a decline in
liquidity in the equity markets due to liquidity shocks from the bond markets. Evidence of uni-
directional causality from equity markets to bond market was observed when a trade value is
used as a liquidity measure at 5 per cent significance level.
Model 3 focuses on interpreting the result of the model specified in chapter 4. The
cointegration and VECM were first conducted to determine the long term relationship
between the foreign investor participation (FIPB and FIPE) of both the bonds and equities
markets. Cointegration was established resulting in the VECM being specified. All the
variables were significant with the exception of EX. Impulse response and Variance
decomposition functions were also constructed to trace the temporal and directional
response of foreign investor participation in equities and bonds, to structural innovations in
116
the macroeconomic variables and as well as tracing the movements in a sequence of the
variables to their own shocks versus shocks to the other variables.
The impulse response function indicated that liquidity in the equity market measured foreign
investor participation is minimally affected by the liquidity shocks from the bond markets
when foreign investor participation is used as a liquidity measure. However there was
evidence of a strong bi-directional causality between liquidity measured by foreign investor
participation both at 1 per cent significance level.
117
CHAPTER 6
CONCLUSIONS AND RECOMMENDATIONS
6.1. Summary of the study and conclusions
Investigating liquidity linkages or commonalities across financial markets has been of a
considerable interest to different role players in these markets, including economist, policy
makers and investors alike. The primary objective of the study was therefore to identify
liquidity linkages between the South African bond and equity markets. The study employed
trade volumes, trade values and foreign investor participation as liquidity measures. Each of
the three measures of liquidity was estimated in the VAR model. These three measures of
liquidity were analysed using the Johansen Cointegration and Vector error correction model.
The reasoning behind using three liquidity measures was to ensure and check for
robustness of the empirical results.
In the first model where trade volumes were used as liquidity measure, the Johansen
Cointegration test provided evidence of one (1) and two (2) cointegrating vectors for the
Trace test and the Max-eigenvalue test. Based on the results of the Johansen test, the
VECM was specified which provided the parameter estimates for the long run relationship.
All the macroeconomic variables, EX, CPI and the repo rate were significant in the long run.
The empirical result revealed that the volumes of bonds trade is positively related to volumes
of equities traded, consistent with the work of Chordia et al (2003). CPI, repo rate and
exchange rate were also found to significant in in the long run. The error correction
estimates also indicated that both the volumes of trade in bond and equities were negative
indicating that these variables are quick to adjust to equilibrium. The impulse response also
showed that these variables do affect each other in that shocks in one market do have an
influence in the other market. However, predominant sources of variations in volumes were
found to be own shocks for both the markets with the macroeconomic variables also having
little impact. Evidence of uni-directional causality was observed running from equities to
bonds at 5 per cent significance level when a volume of trade is employed as a liquidity
measure.
In model two, trade values were used as liquidity measures and there was evidence of
Cointegration between the variables with three (3) and one (1) cointegrating vectors from the
Trace and Max-eigenvalue tests. All the variables were significant and CPI was negative.
The empirical results based on the VECM indicated that trade values in equities were
negatively related to trade values in bonds. Based on the error correction estimates, trade
118
values of stock (LTVE), Repo Rate and exchange rate (EX) were found to have coefficients
that are negative, indicating that any disequilibrium in these variables doesn‘t take long to
adjust. However, CPI, and trade values for bonds have coefficients that are positive
indicating that any disequilibrium in these variables takes long to adjust. There was minimal
impact on trade values for both markets due to shocks from each market. Based on the
variance decomposition, predominant sources of variations in trade values were found to be
own shocks for both the markets with the macroeconomic variables also having little impact.
Evidence of uni-directional causality from equity markets to bond market was observed when
a trade value is used as a liquidity measure at 5 per cent significance level.
Model three (3) tested liquidity commonalities in bond equity markets using foreign investor
participation as a measure of liquidity and there were observed cointegrating vector with two
from trace statistics and the Max-eigenvalues. There was observed negative relationship
between the two measures of liquidity from the VECM results. There little impact from impact
from shock in one market that comes from the other based on the impulse response
analysis. Predominant sources of variations in foreign investor participation were found to be
own shocks for both the markets with the macroeconomic variables also having little impact.
When foreign investor participation is used a measure of liquidity, there is evidence of a
strong bi-directional causality between liquidity measured by foreign investor participation
both at 1 per cent significance level.
In conclusion, the empirical findings of this study do provide evidence of liquidity linkages in
the South African bond and equity markets. Secondly, empirical findings indicates that the
linkages in liquidity between these markets is positive when volumes of trades are used as
liquidity measure and this consistent with studies conducted by Chordia et al (2003 & 2005)
and Engsted and Tanggaard (2000) who found the relationship was a positive one. The
study also indicates that there is bi-directional causality when foreign investor participation is
used as a liquidity measure and this is consistent with Goyenko and Ukhov (2009) although
the authors used different liquidity measures. However, as the main objective was to
establish liquidity linkages between the bond and equity market, empirical results provide
evidence of liquidity integration between stock and bond market liquidity.
119
6.2. Policy implications and recommendations
The study recommends the following:
The authorities should keep inflation at low and stable levels as well as maintain a
stable currency. These will boost bond and equity market liquidity as far as
macroeconomic factors are concerned. This is because bonds are lucrative
investments when inflation is low although it is suggested that stocks are good in
hedging for inflation.
As far as market microstructure factors are concerned, the study identified volume
and trade values as having relationship between these markets. This suggests that
ways to safe-guard against excessive volatility should be encouraged in the bond
and equity markets. The creation of a vibrant derivative market which would allow
effective hedging of interest rate risk as well as credit risk should be encouraged.
This attracts more participants into the market thus deepening the market. Other
tools to reduce the impact of volatility on bond market liquidity include the
development of a more active and well-functioning repurchase market as well as
short-selling transactions. This is consistent with Mares (2002) who proposed that
highly liquid futures markets generates liquidity for the cash market for both bonds
deliverable against futures contracts and the rest of the yield curve.
Development of a private repurchase market is also regarded as one of the ways to
improve the number of participants in the markets. A private repo market could
provide a link between money market and bond market. The private repo is therefore
a tool for market participants to hedge their positions and manage liquidity more
effectively.
More frequent and systematic issuance in the primary market is regarded as one of
the ways to enhance liquidity in the bond and equity market. Re-issuance of bonds
will increase trading volume in the market through the effect of new auctions leading
to an improvement in liquidity in the bond market.
As for volume in the bond markets, one way the government can enhance liquidity is
through bond buy-back program for various issues with small outstanding sizes. In
other words, debt of different maturities can be lumped together to create fewer
maturities. Bodecker (1999) show that, this practise was done in Namibia when the
Ministry of Finance and the Bank of Namibia consolidated the outstanding internal
registered stock in 1998 with the aim of lengthening the maturity structure of
120
domestic public debt as well as increasing the potential for liquidity in the
Government bond market. For the equity markets, volumes can be improved through
some form relaxation in dividends tax and other tax reforms that can be intended to
improve investor participation.
6.3. Limitations of the study
It should be noted that the analysis in this study is only partial in scope. A comprehensive
study which focuses at bond and equity market liquidities and the roles played by these
markets to the broader economy is needed. In addition, the study focused on monthly data,
analysis of the volumes of traded, values of trades and foreign investor participation, focus
on daily or quarterly data may sometimes provide better results. However these
shortcomings do not render our analysis invalid given that the results conform to theory and
are supported by prior empirical studies.
121
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a
8. APPENDICES
APPENDICES FOR MODEL 1
APPENDIX 1 (A): JOHANSEN COINTEGRATION TEST RESULTS
Date: 11/23/12 Time: 17:37
Sample (adjusted): 2000M08 2008M09
Included observations: 98 after adjustments
Trend assumption: Linear deterministic trend
Series: LVOLE LVOLB CPI REPO_RATE EX
Lags interval (in first differences): 1 to 6
Unrestricted Cointegration Rank Test (Trace) Hypothesized Trace 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None * 0.299824 81.41474 69.81889 0.0045
At most 1 0.247979 46.48528 47.85613 0.0669
At most 2 0.121246 18.55621 29.79707 0.5252
At most 3 0.056915 5.889652 15.49471 0.7085
At most 4 0.001499 0.146992 3.841466 0.7014 Trace test indicates 1 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegration Rank Test (Maximum Eigenvalue) Hypothesized Max-Eigen 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None * 0.299824 34.92945 33.87687 0.0373
At most 1 * 0.247979 27.92907 27.58434 0.0452
At most 2 0.121246 12.66656 21.13162 0.4834
At most 3 0.056915 5.742660 14.26460 0.6464
At most 4 0.001499 0.146992 3.841466 0.7014 Max-eigenvalue test indicates 2 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegrating Coefficients (normalized by b'*S11*b=I): LVOLE LVOLB CPI REPO_RATE EX
8.102114 1.755526 -0.866478 0.601957 0.468733
10.88291 -8.863595 0.006515 0.948159 -0.515911
-1.522201 0.134726 0.728755 -1.305257 0.712945
-0.521003 2.836086 0.000841 -0.225193 -0.733929
-3.580394 6.560532 0.199938 -0.181199 -0.196911
Unrestricted Adjustment Coefficients (alpha): D(LVOLE) -0.038794 -0.009978 0.002513 0.020856 0.000143
D(LVOLB) -0.027695 0.033752 0.004423 0.017792 -0.002595
b
D(CPI) 0.113408 -0.119784 -0.008925 0.038997 -0.006897
D(REPO_RATE) 0.075836 -0.012049 0.083719 0.016448 0.001277
D(EX) 0.032238 0.091573 -0.032272 0.030481 0.004819
1 Cointegrating Equation(s): Log likelihood 101.9335 Normalized cointegrating coefficients (standard error in parentheses)
LVOLE LVOLB CPI REPO_RATE EX
1.000000 0.216675 -0.106945 0.074296 0.057853
(0.20095) (0.02246) (0.02780) (0.02895)
Adjustment coefficients (standard error in parentheses)
D(LVOLE) -0.314317
(0.10752)
D(LVOLB) -0.224384
(0.12849)
D(CPI) 0.918846
(0.38239)
D(REPO_RATE) 0.614428
(0.27793)
D(EX) 0.261195
(0.27611)
2 Cointegrating Equation(s): Log likelihood 115.8980 Normalized cointegrating coefficients (standard error in parentheses)
LVOLE LVOLB CPI REPO_RATE EX
1.000000 0.000000 -0.084346 0.076992 0.035735
(0.01476) (0.02250) (0.02331)
0.000000 1.000000 -0.104297 -0.012440 0.102081
(0.02318) (0.03534) (0.03661)
Adjustment coefficients (standard error in parentheses)
D(LVOLE) -0.422911 0.020340
(0.17929) (0.11940)
D(LVOLB) 0.142939 -0.347785
(0.20766) (0.13830)
D(CPI) -0.384757 1.260812
(0.60829) (0.40511)
D(REPO_RATE) 0.483297 0.239931
(0.46498) (0.30967)
D(EX) 1.257776 -0.755072
(0.43635) (0.29060)
3 Cointegrating Equation(s): Log likelihood 122.2313 Normalized cointegrating coefficients (standard error in parentheses)
LVOLE LVOLB CPI REPO_RATE EX
1.000000 0.000000 0.000000 -0.085874 0.139186
(0.04099) (0.06274)
0.000000 1.000000 0.000000 -0.213829 0.230003
(0.05231) (0.08008)
0.000000 0.000000 1.000000 -1.930916 1.226512
(0.43732) (0.66944)
Adjustment coefficients (standard error in parentheses)
D(LVOLE) -0.426736 0.020679 0.035381
(0.18036) (0.11938) (0.01496)
D(LVOLB) 0.136206 -0.347189 0.027440
c
(0.20883) (0.13822) (0.01732)
D(CPI) -0.371172 1.259610 -0.105550
(0.61192) (0.40503) (0.05075)
D(REPO_RATE) 0.355860 0.251210 -0.004778
(0.44625) (0.29537) (0.03701)
D(EX) 1.306899 -0.759419 -0.050855
(0.43572) (0.28840) (0.03613)
4 Cointegrating Equation(s): Log likelihood 125.1027 Normalized cointegrating coefficients (standard error in parentheses)
LVOLE LVOLB CPI REPO_RATE EX
1.000000 0.000000 0.000000 0.000000 -0.194719
(0.11333)
0.000000 1.000000 0.000000 0.000000 -0.601435
(0.27979)
0.000000 0.000000 1.000000 0.000000 -6.281526
(2.55003)
0.000000 0.000000 0.000000 1.000000 -3.888329
(1.36428)
Adjustment coefficients (standard error in parentheses)
D(LVOLE) -0.437602 0.079827 0.035398 -0.040790
(0.17705) (0.12274) (0.01467) (0.02250)
D(LVOLB) 0.126937 -0.296729 0.027455 0.005552
(0.20683) (0.14338) (0.01714) (0.02629)
D(CPI) -0.391490 1.370208 -0.105517 -0.042441
(0.60884) (0.42207) (0.05045) (0.07739)
D(REPO_RATE) 0.347291 0.297859 -0.004764 -0.078754
(0.44571) (0.30898) (0.03694) (0.05665)
D(EX) 1.291019 -0.672972 -0.050829 0.141490
(0.43302) (0.30018) (0.03588) (0.05504)
APPENDIX 1 (B): VECTOR ERROR CORRECTION ESTIMATES RESULTS
Vector Error Correction Estimates
Date: 11/23/12 Time: 17:39
Sample (adjusted): 2000M08 2008M09
Included observations: 98 after adjustments
Standard errors in ( ) & t-statistics in [ ] Cointegrating Eq: CointEq1 LVOLE(-1) 1.000000
LVOLB(-1) 0.216675
(0.20095)
[ 1.07826]
CPI(-1) -0.106945
(0.02246)
[-4.76151]
REPO_RATE(-1) 0.074296
(0.02780)
[ 2.67264]
EX(-1) 0.057853
d
(0.02895)
[ 1.99825]
C -25.80140
Error Correction: D(LVOLE) D(LVOLB) D(CPI) D(REPO_RATE
) D(EX) CointEq1 -0.314317 -0.224384 0.918846 0.614428 0.261195
(0.10752) (0.12849) (0.38239) (0.27793) (0.27611)
[-2.92322] [-1.74628] [ 2.40293] [ 2.21073] [ 0.94598]
D(LVOLE(-1)) -0.548262 -0.197458 -0.387725 -0.588522 -0.382242
(0.15534) (0.18564) (0.55244) (0.40153) (0.39890)
[-3.52940] [-1.06369] [-0.70184] [-1.46570] [-0.95824]
D(LVOLE(-2)) -0.256773 0.063372 -0.830746 -0.158141 -0.383145
(0.16709) (0.19967) (0.59422) (0.43190) (0.42907)
[-1.53674] [ 0.31738] [-1.39805] [-0.36616] [-0.89297]
D(LVOLE(-3)) 0.189833 0.011709 -0.025002 -0.434145 0.160658
(0.17422) (0.20820) (0.61958) (0.45033) (0.44738)
[ 1.08961] [ 0.05624] [-0.04035] [-0.96406] [ 0.35911]
D(LVOLE(-4)) 0.331920 0.416892 -0.401602 -0.083790 0.497726
(0.16735) (0.19999) (0.59515) (0.43258) (0.42974)
[ 1.98336] [ 2.08458] [-0.67479] [-0.19370] [ 1.15819]
D(LVOLE(-5)) 0.506793 0.530845 0.230853 -0.085759 0.036130
(0.15475) (0.18492) (0.55032) (0.39999) (0.39737)
[ 3.27501] [ 2.87063] [ 0.41949] [-0.21440] [ 0.09092]
D(LVOLE(-6)) 0.489290 0.156812 0.291723 0.284462 -0.441607
(0.14029) (0.16765) (0.49891) (0.36262) (0.36025)
[ 3.48771] [ 0.93536] [ 0.58472] [ 0.78445] [-1.22584]
D(LVOLB(-1)) 0.022992 -0.256733 -0.967335 -0.312283 0.329169
(0.12345) (0.14753) (0.43904) (0.31911) (0.31702)
[ 0.18624] [-1.74022] [-2.20331] [-0.97862] [ 1.03833]
D(LVOLB(-2)) 0.044594 -0.336633 -0.368343 -0.264962 0.339350
(0.12923) (0.15443) (0.45958) (0.33404) (0.33185)
[ 0.34507] [-2.17980] [-0.80147] [-0.79321] [ 1.02260]
D(LVOLB(-3)) -0.013031 0.135912 -0.427539 0.070123 0.155581
(0.13673) (0.16339) (0.48624) (0.35341) (0.35110)
[-0.09531] [ 0.83183] [-0.87928] [ 0.19842] [ 0.44313]
D(LVOLB(-4)) 0.097008 -0.023545 0.196486 -0.459611 0.053445
(0.13600) (0.16252) (0.48366) (0.35154) (0.34924)
[ 0.71329] [-0.14487] [ 0.40625] [-1.30743] [ 0.15303]
D(LVOLB(-5)) -0.064163 -0.039264 0.183219 -0.028782 0.095225
(0.12156) (0.14526) (0.43229) (0.31421) (0.31215)
[-0.52784] [-0.27029] [ 0.42383] [-0.09160] [ 0.30506]
D(LVOLB(-6)) -0.219937 -0.055818 -0.143668 -0.517278 0.005571
(0.11918) (0.14242) (0.42383) (0.30805) (0.30603)
[-1.84546] [-0.39193] [-0.33898] [-1.67919] [ 0.01820]
D(CPI(-1)) 0.051332 0.012012 0.373292 -0.068030 0.079380
(0.03452) (0.04125) (0.12277) (0.08923) (0.08865)
e
[ 1.48697] [ 0.29118] [ 3.04063] [-0.76239] [ 0.89546]
D(CPI(-2)) -0.059946 -0.063482 -0.031600 0.265591 0.000964
(0.03375) (0.04033) (0.12002) (0.08723) (0.08666)
[-1.77629] [-1.57409] [-0.26329] [ 3.04464] [ 0.01112]
D(CPI(-3)) -0.028985 -0.029848 0.112968 -0.036984 0.114184
(0.03553) (0.04246) (0.12636) (0.09185) (0.09124)
[-0.81572] [-0.70295] [ 0.89399] [-0.40268] [ 1.25142]
D(CPI(-4)) -0.048015 -0.026155 -0.011326 0.138707 -0.040370
(0.03334) (0.03984) (0.11857) (0.08618) (0.08562)
[-1.44008] [-0.65642] [-0.09552] [ 1.60944] [-0.47151]
D(CPI(-5)) -0.003356 -0.023169 0.392562 0.028138 0.010795
(0.03133) (0.03744) (0.11141) (0.08097) (0.08044)
[-0.10714] [-0.61888] [ 3.52364] [ 0.34749] [ 0.13420]
D(CPI(-6)) -0.042012 0.008682 -0.107413 0.082206 0.022792
(0.02899) (0.03464) (0.10308) (0.07492) (0.07443)
[-1.44942] [ 0.25064] [-1.04204] [ 1.09722] [ 0.30622]
D(REPO_RATE(-1)) -0.010298 0.011181 0.496758 0.136768 0.007586
(0.04837) (0.05780) (0.17202) (0.12503) (0.12421)
[-0.21290] [ 0.19343] [ 2.88784] [ 1.09390] [ 0.06107]
D(REPO_RATE(-2)) -0.020347 0.047816 -0.403570 0.078769 -0.044338
(0.05152) (0.06157) (0.18322) (0.13317) (0.13230)
[-0.39494] [ 0.77665] [-2.20266] [ 0.59149] [-0.33514]
D(REPO_RATE(-3)) 0.051443 0.066351 0.200955 -0.243084 -0.064380
(0.05237) (0.06258) (0.18624) (0.13537) (0.13448)
[ 0.98229] [ 1.06021] [ 1.07900] [-1.79574] [-0.47873]
D(REPO_RATE(-4)) 0.006394 0.101536 0.317973 0.307984 -0.107014
(0.05099) (0.06093) (0.18133) (0.13180) (0.13093)
[ 0.12540] [ 1.66639] [ 1.75356] [ 2.33683] [-0.81732]
D(REPO_RATE(-5)) 0.040306 -0.039302 -0.371438 -0.288394 -0.192819
(0.05342) (0.06383) (0.18997) (0.13807) (0.13717)
[ 0.75454] [-0.61568] [-1.95527] [-2.08869] [-1.40570]
D(REPO_RATE(-6)) 0.045523 0.029989 -0.035176 -0.036713 0.022930
(0.05666) (0.06771) (0.20151) (0.14646) (0.14550)
[ 0.80342] [ 0.44290] [-0.17457] [-0.25067] [ 0.15760]
D(EX(-1)) 0.064068 0.069266 0.407397 0.058946 0.272684
(0.05386) (0.06437) (0.19155) (0.13922) (0.13831)
[ 1.18947] [ 1.07612] [ 2.12684] [ 0.42338] [ 1.97150]
D(EX(-2)) -0.069199 -0.005397 -0.103025 -0.067388 -0.096976
(0.05361) (0.06406) (0.19064) (0.13856) (0.13766)
[-1.29087] [-0.08424] [-0.54042] [-0.48633] [-0.70448]
D(EX(-3)) 0.006462 -0.014229 0.155969 -0.027781 0.031657
(0.05303) (0.06338) (0.18860) (0.13708) (0.13618)
[ 0.12184] [-0.22452] [ 0.82698] [-0.20266] [ 0.23246]
D(EX(-4)) 0.091786 0.065600 -0.236185 0.028206 -0.095710
(0.05260) (0.06286) (0.18707) (0.13597) (0.13508)
[ 1.74485] [ 1.04355] [-1.26252] [ 0.20744] [-0.70854]
f
D(EX(-5)) 0.122056 0.093020 -0.287225 -0.149564 -0.166853
(0.05351) (0.06395) (0.19030) (0.13831) (0.13741)
[ 2.28098] [ 1.45468] [-1.50934] [-1.08133] [-1.21428]
D(EX(-6)) 0.065596 0.008522 0.166198 0.232169 0.079227
(0.05676) (0.06783) (0.20185) (0.14671) (0.14575)
[ 1.15569] [ 0.12564] [ 0.82337] [ 1.58248] [ 0.54358]
C 0.012503 0.017533 0.026174 -0.021453 -0.007206
(0.01446) (0.01728) (0.05144) (0.03739) (0.03714)
[ 0.86442] [ 1.01439] [ 0.50886] [-0.57381] [-0.19402] R-squared 0.608605 0.554620 0.694217 0.522574 0.333220
Adj. R-squared 0.424769 0.345426 0.550591 0.298328 0.020036
Sum sq. resids 1.139162 1.626789 14.40716 7.611064 7.511716
S.E. equation 0.131377 0.156998 0.467216 0.339587 0.337363
F-statistic 3.310574 2.651223 4.833522 2.330363 1.063974
Log likelihood 79.22310 61.76364 -45.11112 -13.84311 -13.19930
Akaike AIC -0.963737 -0.607421 1.573696 0.935574 0.922435
Schwarz SC -0.119666 0.236650 2.417767 1.779645 1.766506
Mean dependent 0.006907 0.009219 0.072449 0.002551 0.011949
S.D. dependent 0.173221 0.194050 0.696942 0.405400 0.340794 Determinant resid covariance (dof adj.) 6.20E-07
Determinant resid covariance 8.59E-08
Log likelihood 101.9335
Akaike information criterion 1.287071
Schwarz criterion 5.639312
APPENDIX 1 (C): CORRELATION MATRIX
LVOLE LVOLB CPI REPO_RATE EX
LVOLE 1.000000 0.540986 0.193348 0.049874 0.145888
LVOLB 0.540986 1.000000 0.003365 -0.015575 0.291246
CPI 0.193348 0.003365 1.000000 0.235246 -0.326041
REPO_RATE 0.049874 -0.015575 0.235246 1.000000 -0.105103
EX 0.145888 0.291246 -0.326041 -0.105103 1.000000
APPENDIX 1 (D): VARIANCE DECOMPOSITION
Varian
ce Decomposition
of LVOLE:
Period S.E. LVOLE LVOLB CPI REPO_RATE EX 1 0.131377 100.0000 0.000000 0.000000 0.000000 0.000000
2 0.138333 93.10276 0.203986 4.996295 0.690588 1.006375
3 0.145995 92.24519 0.773965 4.938900 0.628538 1.413405
4 0.158848 92.23694 1.319067 4.422134 0.680933 1.340925
5 0.176236 91.41212 1.101834 3.635086 0.758091 3.092870
g
6 0.192000 89.16436 1.182686 3.619470 0.649898 5.383587
7 0.204715 85.97543 3.546611 3.781410 0.576566 6.119981
8 0.210341 84.24825 3.359860 4.605819 0.737442 7.048633
9 0.227794 83.98881 3.119091 4.623870 0.700580 7.567645
10 0.240188 80.50087 4.117529 5.630787 0.938850 8.811963
11 0.247615 77.62130 4.338990 7.654222 1.542114 8.843374
12 0.257266 75.10431 5.115792 9.726741 1.556705 8.496451
13 0.268115 73.22054 5.653838 10.48193 1.933192 8.710504
14 0.277134 70.49809 5.619075 12.87890 2.393946 8.609989
15 0.286485 68.11354 6.504509 14.74466 2.397800 8.239498
16 0.294264 66.03835 7.076083 16.42092 2.467329 7.997322
17 0.302856 64.69704 6.974811 18.06981 2.629135 7.629204
18 0.311987 63.19669 7.471532 19.41354 2.566659 7.351579
19 0.319573 61.73378 7.900321 20.64733 2.609289 7.109282
20 0.326869 60.55947 7.934866 22.11903 2.576165 6.810463
21 0.335137 60.03542 8.172011 22.71433 2.506924 6.571307
22 0.342964 59.38675 8.306698 23.36389 2.483868 6.458794
23 0.350009 58.84589 8.322986 24.11223 2.446365 6.272531
24 0.357215 58.48737 8.491978 24.52011 2.369955 6.130585
25 0.364690 58.45880 8.467240 24.67568 2.342709 6.055571
26 0.371993 58.39526 8.390396 24.92061 2.302383 5.991351
27 0.379125 58.32684 8.456679 25.00683 2.257521 5.952129
28 0.385891 58.31006 8.443755 25.07541 2.230051 5.940718
29 0.392809 58.44976 8.347006 25.06643 2.212398 5.924409
30 0.399956 58.53336 8.327958 24.98095 2.184457 5.973273
31 0.406551 58.54393 8.299907 24.95086 2.183903 6.021408
32 0.412839 58.54923 8.254402 24.98017 2.176702 6.039496
33 0.419402 58.60903 8.232743 24.90393 2.172428 6.081868
34 0.425773 58.58897 8.202554 24.88020 2.183190 6.145088
35 0.431798 58.51012 8.183906 24.93907 2.196772 6.170135
36 0.437747 58.40356 8.207742 24.99337 2.201538 6.193798 Varian
ce Decomposition
of LVOLB:
Period S.E. LVOLE LVOLB CPI REPO_RATE EX 1 0.156998 29.26656 70.73344 0.000000 0.000000 0.000000
2 0.184589 21.43667 77.42435 0.273071 0.015428 0.850477
3 0.197243 20.32153 76.72025 0.874846 1.072638 1.010740
4 0.225627 17.69239 78.73049 1.004051 1.785539 0.787537
5 0.257541 19.56457 73.62373 0.773995 4.778640 1.259065
6 0.279007 21.91761 69.19490 0.858032 4.989328 3.040124
7 0.291997 20.43369 70.54876 1.270851 4.793274 2.953427
8 0.307749 19.26525 71.41675 1.305736 5.202753 2.809511
9 0.325501 21.72140 67.74206 1.167298 5.463545 3.905700
10 0.340562 21.59366 67.08408 1.371978 5.072872 4.877410
11 0.352821 21.02357 66.63409 1.885816 5.234246 5.222283
12 0.363979 21.30957 65.66624 2.162953 5.361721 5.499509
13 0.379354 21.58199 65.29047 2.377479 5.045644 5.704410
14 0.392955 21.87665 64.66272 2.722915 4.862845 5.874872
15 0.403203 22.30807 63.63885 2.988607 4.930586 6.133896
16 0.414999 22.14183 63.74399 3.241100 4.825711 6.047369
17 0.428667 22.76805 63.12455 3.368908 4.744284 5.994205
18 0.441148 23.55862 62.07745 3.374084 4.684190 6.305666
19 0.452915 23.57911 61.95719 3.450007 4.618640 6.395052
20 0.464781 23.84940 61.64705 3.485820 4.665384 6.352349
21 0.477364 24.56628 60.82454 3.387659 4.651870 6.569645
22 0.490354 24.88412 60.48913 3.343974 4.541911 6.740867
h
23 0.501869 25.18177 60.10690 3.327960 4.541984 6.841395
24 0.512671 25.50755 59.67226 3.262117 4.533696 7.024381
25 0.524836 25.78617 59.41443 3.195401 4.444596 7.159403
26 0.536509 26.12767 59.01040 3.145642 4.394423 7.321860
27 0.546869 26.36427 58.63726 3.102302 4.353204 7.542966
28 0.557319 26.45450 58.52071 3.080102 4.292396 7.652294
29 0.567826 26.66587 58.27575 3.047593 4.248123 7.762657
30 0.577892 26.86777 57.98399 3.017796 4.193071 7.937368
31 0.587505 26.92517 57.87439 3.021769 4.138141 8.040533
32 0.596604 26.99900 57.74809 3.031634 4.109744 8.111531
33 0.605696 27.11180 57.58022 3.029396 4.070843 8.207739
34 0.614802 27.16646 57.48566 3.044623 4.024591 8.278664
35 0.623333 27.21474 57.37704 3.071886 3.999865 8.336473
36 0.631581 27.25574 57.27688 3.096941 3.978332 8.392108 Varian
ce Decomposition of CPI:
Period S.E. LVOLE LVOLB CPI REPO_RATE EX 1 0.467216 3.738356 1.448849 94.81279 0.000000 0.000000
2 0.800925 4.871166 3.708860 83.20319 5.192109 3.024672
3 0.999609 5.513928 4.043177 78.23151 5.727044 6.484346
4 1.221877 10.13802 3.174787 71.69976 6.413813 8.573624
5 1.452878 14.08870 2.289407 64.93500 9.268564 9.418327
6 1.723005 19.39973 1.697419 62.14065 9.106840 7.655365
7 1.995544 25.30206 1.426184 58.68000 8.073939 6.517819
8 2.223149 30.76363 1.213126 53.59209 7.472941 6.958216
9 2.467076 37.12441 0.988933 47.29386 7.101380 7.491422
10 2.726366 43.03878 0.874146 40.76701 6.924859 8.395201
11 3.008217 48.16543 0.861655 34.86793 6.489906 9.615082
12 3.315384 52.72628 0.905134 29.48964 6.019443 10.85950
13 3.603368 55.81153 1.029093 25.22390 5.628100 12.30738
14 3.893579 58.27885 1.197548 21.68880 5.209228 13.62557
15 4.187954 60.21088 1.334976 18.77768 4.814597 14.86187
16 4.464879 61.47188 1.455497 16.54298 4.421250 16.10840
17 4.726861 62.33064 1.527800 14.77732 4.060138 17.30410
18 4.970236 62.76601 1.568137 13.37013 3.747823 18.54789
19 5.194111 62.94540 1.625522 12.25021 3.475696 19.70318
20 5.401052 63.04627 1.650948 11.35212 3.246099 20.70457
21 5.584793 63.01951 1.642141 10.66719 3.052324 21.61884
22 5.747215 62.93586 1.629113 10.16431 2.888932 22.38178
23 5.892534 62.82688 1.602329 9.795214 2.753354 23.02223
24 6.023069 62.68353 1.567931 9.547321 2.638402 23.56282
25 6.141313 62.52660 1.532761 9.428797 2.540631 23.97121
26 6.249789 62.36088 1.492926 9.413326 2.457317 24.27555
27 6.351199 62.18898 1.453312 9.473499 2.384435 24.49977
28 6.447297 62.02787 1.416530 9.588904 2.321377 24.64532
29 6.539758 61.88200 1.380572 9.741286 2.268515 24.72762
30 6.630449 61.75991 1.346048 9.916162 2.223818 24.75406
31 6.720408 61.66813 1.313511 10.09491 2.186891 24.73656
32 6.810742 61.61428 1.282168 10.25154 2.157211 24.69479
33 6.902044 61.59922 1.252774 10.37288 2.134164 24.64096
34 6.994838 61.62399 1.226257 10.45517 2.117098 24.57749
35 7.090267 61.68906 1.201876 10.49278 2.104406 24.51188
36 7.188440 61.78721 1.180378 10.48452 2.093316 24.45457 Varian
ce Decom
i
position of
REPO_RATE:
Period S.E. LVOLE LVOLB CPI REPO_RATE EX 1 0.339587 0.248741 0.256035 5.082175 94.41305 0.000000
2 0.518554 0.106902 0.409833 2.314836 96.86469 0.303739
3 0.677694 1.162554 0.569442 3.338695 94.72480 0.204508
4 0.795511 1.873726 0.445338 3.688974 93.65097 0.340987
5 0.941761 2.534629 0.788507 4.628144 91.57796 0.470757
6 1.050925 4.426655 1.040929 4.702776 89.31450 0.515138
7 1.165002 7.138307 1.299979 4.692958 85.61745 1.251310
8 1.281583 11.39014 1.178428 4.353993 80.59935 2.478086
9 1.409376 15.51477 1.006171 3.830945 75.84665 3.801462
10 1.525663 19.63144 0.873733 3.298948 71.27378 4.922106
11 1.669331 24.50186 0.770067 2.763208 65.68137 6.283495
12 1.813917 28.84789 0.786778 2.448204 60.47187 7.445257
13 1.958399 32.30213 0.841812 2.294399 56.06372 8.497938
14 2.103896 35.27528 0.914448 2.382704 51.93258 9.494986
15 2.253109 37.55578 1.010322 2.606412 48.18154 10.64595
16 2.397085 39.29733 1.155416 2.896910 44.91582 11.73453
17 2.536892 40.46040 1.211859 3.206864 42.20402 12.91686
18 2.665169 41.24724 1.281426 3.494843 39.93232 14.04418
19 2.784389 41.78631 1.343471 3.696253 38.08310 15.09087
20 2.891800 42.15886 1.365298 3.846698 36.55954 16.06961
21 2.986128 42.35542 1.366143 3.917673 35.37230 16.98846
22 3.067306 42.47560 1.363610 3.926934 34.47893 17.75492
23 3.139748 42.53409 1.339505 3.890971 33.79660 18.43883
24 3.202865 42.55782 1.314094 3.824760 33.27285 19.03048
25 3.258311 42.53435 1.285948 3.736462 32.92694 19.51630
26 3.307916 42.49484 1.255166 3.643348 32.70943 19.89721
27 3.353531 42.44391 1.225560 3.550269 32.58634 20.19392
28 3.395990 42.39659 1.197852 3.462510 32.53872 20.40433
29 3.436941 42.33992 1.170140 3.380695 32.55886 20.55038
30 3.476608 42.28915 1.144154 3.305279 32.62347 20.63794
31 3.516470 42.25109 1.118999 3.232985 32.72041 20.67652
32 3.557244 42.23444 1.094081 3.161012 32.82670 20.68377
33 3.599086 42.23268 1.069912 3.088907 32.93812 20.67038
34 3.642298 42.25600 1.046949 3.016192 33.04456 20.63630
35 3.687643 42.30666 1.024663 2.942751 33.13030 20.59563
36 3.734883 42.38621 1.004714 2.871001 33.18172 20.55636 Varian
ce Decomposition of EX:
Period S.E. LVOLE LVOLB CPI REPO_RATE EX 1 0.337363 2.128344 6.373315 11.06242 0.064221 80.37170
2 0.558304 3.114055 10.26109 8.584620 0.024869 78.01537
3 0.734856 3.404309 13.83988 7.691296 0.016922 75.04759
4 0.894008 5.612626 15.34667 6.493337 0.022613 72.52475
5 1.044649 9.261142 15.79500 5.699668 0.066185 69.17800
6 1.157570 11.05889 16.89738 5.419039 0.590135 66.03456
7 1.251421 11.94331 17.61386 5.320234 1.175814 63.94679
8 1.356515 13.47487 17.80509 5.716209 1.514221 61.48961
9 1.467378 14.54967 18.13111 6.026498 1.792549 59.50017
10 1.579949 15.22992 18.15550 6.407349 1.896870 58.31037
11 1.689973 15.42928 18.02789 6.929061 1.846966 57.76680
12 1.795124 15.52903 18.37826 7.324115 1.830742 56.93785
j
13 1.894049 15.65991 18.58584 7.588958 1.817999 56.34729
14 1.983844 15.68075 18.47225 7.787893 1.875433 56.18367
15 2.061591 15.46774 18.50331 7.856099 1.982596 56.19025
16 2.132795 15.23857 18.51223 7.905344 2.098116 56.24574
17 2.201810 15.01102 18.38962 7.897549 2.227089 56.47472
18 2.265811 14.72005 18.36137 7.806439 2.380323 56.73181
19 2.322010 14.41512 18.35558 7.679893 2.500684 57.04873
20 2.374133 14.15235 18.32551 7.556605 2.617440 57.34809
21 2.423222 13.90374 18.36764 7.413941 2.737195 57.57749
22 2.467997 13.69089 18.41545 7.269098 2.843598 57.78097
23 2.509109 13.49663 18.43661 7.134093 2.932425 58.00024
24 2.548323 13.31985 18.50775 7.005127 3.017712 58.14957
25 2.586715 13.18522 18.57631 6.883472 3.078336 58.27667
26 2.624937 13.07519 18.61973 6.775543 3.122869 58.40667
27 2.662554 12.97185 18.69395 6.674101 3.154175 58.50592
28 2.700114 12.90603 18.77039 6.585620 3.169299 58.56866
29 2.738789 12.87246 18.82558 6.510764 3.174203 58.61699
30 2.778317 12.85446 18.89241 6.444932 3.176463 58.63173
31 2.818149 12.85337 18.94801 6.392614 3.169761 58.63625
32 2.859016 12.86936 18.98834 6.356648 3.160044 58.62561
33 2.901137 12.89839 19.03335 6.328560 3.149314 58.59039
34 2.944055 12.93778 19.06378 6.310720 3.136786 58.55093
35 2.987457 12.97358 19.07999 6.302896 3.124579 58.51895
36 3.031179 13.00602 19.10264 6.300851 3.115021 58.47546 Choles
ky Orderin
g: LVOLE LVOLB
CPI REPO_RATE
EX
APPENDIX 1 (E): VEC RESIDUAL SERIAL CORRELATION TESTS
VEC Residual Serial Correlation LM Tests Null Hypothesis: no serial correlation at lag order h
Date: 11/23/12 Time: 17:49
Sample: 2000M01 2008M09
Included observations: 98 Lags LM-Stat Prob 1 30.57586 0.2034
2 27.79976 0.3172
3 19.37026 0.7790
4 9.639983 0.9975
5 29.85335 0.2298
6 23.42625 0.5527
7 18.79061 0.8070
8 22.85505 0.5860
9 24.15744 0.5103
10 27.17003 0.3474
11 19.87913 0.7531
12 28.05704 0.3052
k
Probs from chi-square with 25 df.
APPENDIX 1 (F): VEC RESIDUAL NOMARLITY TESTS
VEC Residual Normality Tests
Orthogonalization: Cholesky (Lutkepohl)
Null Hypothesis: residuals are multivariate normal
Date: 11/23/12 Time: 17:49
Sample: 2000M01 2008M09
Included observations: 98
Component Skewness Chi-sq df Prob. 1 -0.517451 4.373341 1 0.0365
2 0.542027 4.798619 1 0.0285
3 0.136908 0.306147 1 0.5801
4 -0.446675 3.258796 1 0.0710
5 1.081460 19.10274 1 0.0000 Joint 31.83964 5 0.0000
Component Kurtosis Chi-sq df Prob. 1 4.301450 6.916235 1 0.0085
2 6.147804 40.46040 1 0.0000
3 2.621601 0.584675 1 0.4445
4 3.363161 0.538535 1 0.4630
5 7.825485 95.08167 1 0.0000 Joint 143.5815 5 0.0000
Component Jarque-Bera df Prob. 1 11.28958 2 0.0035
2 45.25902 2 0.0000
3 0.890822 2 0.6406
4 3.797331 2 0.1498
5 114.1844 2 0.0000 Joint 175.4212 10 0.0000
APPENDIX 1 (G): VEC Residual Heteroskedasticity Test: No Cross Terms (only levels
and squares)
VEC Residual Heteroskedasticity Tests: No Cross Terms (only levels and squares)
Date: 11/23/12 Time: 17:50
Sample: 2000M01 2008M09
Included observations: 98
Joint test:
l
Chi-sq df Prob. 950.4427 930 0.3135
Individual components: Dependent R-squared F(62,35) Prob. Chi-sq(62) Prob. res1*res1 0.613543 0.896232 0.6532 60.12723 0.5437
res2*res2 0.689538 1.253795 0.2370 67.57475 0.2926
res3*res3 0.747372 1.670055 0.0514 73.24241 0.1554
res4*res4 0.676451 1.180246 0.3019 66.29219 0.3312
res5*res5 0.638078 0.995256 0.5174 62.53162 0.4572
res2*res1 0.471071 0.502765 0.9911 46.16495 0.9337
res3*res1 0.601883 0.853451 0.7118 58.98458 0.5852
res3*res2 0.564707 0.732349 0.8593 55.34129 0.7124
res4*res1 0.519997 0.611552 0.9548 50.95973 0.8405
res4*res2 0.603840 0.860453 0.7023 59.17630 0.5782
res4*res3 0.723655 1.478277 0.1064 70.91817 0.2048
res5*res1 0.663715 1.114166 0.3707 65.04404 0.3712
res5*res2 0.497412 0.558703 0.9775 48.74642 0.8900
res5*res3 0.659583 1.093793 0.3939 64.63916 0.3846
res5*res4 0.718603 1.441605 0.1219 70.42310 0.2165
APPENDIX 1 (H): VEC Granger Causality/Block Exogeneity Wald Tests
VEC Granger Causality/Block Exogeneity Wald Tests
Date: 01/08/13 Time: 23:10
Sample: 2000M01 2008M09
Included observations: 98
Dependent variable: D(LVOLB) Excluded Chi-sq df Prob. D(LVOLE) 15.71183 6 0.0154
D(CPI) 5.195197 6 0.5190
D(EX) 5.344428 6 0.5005 D(REPO_RATE
) 6.496243 6 0.3700 All 35.95218 24 0.0555
Dependent variable: D(LVOLE) Excluded Chi-sq df Prob. D(LVOLB) 8.305785 6 0.2165
D(CPI) 7.976389 6 0.2398
D(EX) 15.23292 6 0.0185 D(REPO_RATE
) 2.423099 6 0.8770 All 26.77840 24 0.3149
m
Dependent variable: D(CPI) Excluded Chi-sq df Prob. D(LVOLB) 5.883911 6 0.4363
D(LVOLE) 5.317001 6 0.5038
D(EX) 11.35285 6 0.0781 D(REPO_RATE
) 18.24302 6 0.0057 All 51.41501 24 0.0009
Dependent variable: D(EX) Excluded Chi-sq df Prob. D(LVOLB) 1.846713 6 0.9332
D(LVOLE) 6.829051 6 0.3369
D(CPI) 3.227905 6 0.7797 D(REPO_RATE
) 3.362366 6 0.7622 All 17.10359 24 0.8442
Dependent variable: D(REPO_RATE) Excluded Chi-sq df Prob. D(LVOLB) 6.354135 6 0.3847
D(LVOLE) 4.178507 6 0.6525
D(CPI) 11.55158 6 0.0728
D(EX) 3.820067 6 0.7010 All 35.29153 24 0.0642
APPENDICES FOR MODEL 2
APPENDIX 2 (A): JOHANSEN COINTEGRATION TEST RESULTS
Date: 11/23/12 Time: 18:17
Sample (adjusted): 2000M10 2008M09
Included observations: 96 after adjustments
Trend assumption: Linear deterministic trend
Series: LTVE LTVB CPI EX REPO_RATE
Lags interval (in first differences): 1 to 8
Unrestricted Cointegration Rank Test (Trace) Hypothesized Trace 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.**
n
None * 0.333986 99.60539 69.81889 0.0000
At most 1 * 0.234864 60.58670 47.85613 0.0021
At most 2 * 0.187519 34.88735 29.79707 0.0119
At most 3 0.104079 14.95169 15.49471 0.0602
At most 4 * 0.044809 4.401008 3.841466 0.0359 Trace test indicates 3 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegration Rank Test (Maximum Eigenvalue) Hypothesized Max-Eigen 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None * 0.333986 39.01868 33.87687 0.0111
At most 1 0.234864 25.69935 27.58434 0.0854
At most 2 0.187519 19.93566 21.13162 0.0728
At most 3 0.104079 10.55068 14.26460 0.1782
At most 4 * 0.044809 4.401008 3.841466 0.0359 Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegrating Coefficients (normalized by b'*S11*b=I): LTVE LTVB CPI EX REPO_RATE
14.74515 -24.69029 -1.549988 2.586082 3.036024
-8.265649 23.41445 0.007430 -0.205161 -1.815909
4.784233 -6.348714 -1.522553 0.250765 2.503138
-1.193548 -0.472943 0.417753 0.459222 -0.297668
-1.773073 7.563461 0.283657 -0.817055 -0.047657
Unrestricted Adjustment Coefficients (alpha): D(LTVE) -0.014721 0.008480 -0.030119 -0.018215 -0.010101
D(LTVB) 0.006402 -0.042525 -0.022639 -0.018839 0.000321
D(CPI) 0.099712 0.089978 0.044245 -0.078207 -0.002892
D(EX) -0.084197 -0.013633 -0.028525 -0.028664 0.027250
D(REPO_RATE) 0.088244 0.055015 -0.070188 0.001081 0.019273
1 Cointegrating Equation(s): Log likelihood 153.1227 Normalized cointegrating coefficients (standard error in parentheses)
LTVE LTVB CPI EX REPO_RATE
1.000000 -1.674468 -0.105119 0.175385 0.205900
(0.13228) (0.01781) (0.01870) (0.01947)
Adjustment coefficients (standard error in parentheses)
D(LTVE) -0.217062
(0.21084)
D(LTVB) 0.094391
(0.23680)
D(CPI) 1.470265
(0.70570)
D(EX) -1.241495
(0.41962)
D(REPO_RATE) 1.301170
o
(0.50359)
2 Cointegrating Equation(s): Log likelihood 165.9723 Normalized cointegrating coefficients (standard error in parentheses)
LTVE LTVB CPI EX REPO_RATE
1.000000 0.000000 -0.255785 0.393050 0.185959
(0.05173) (0.07410) (0.07562)
0.000000 1.000000 -0.089978 0.129990 -0.011909
(0.02709) (0.03881) (0.03960)
Adjustment coefficients (standard error in parentheses)
D(LTVE) -0.287155 0.562018
(0.24092) (0.48496)
D(LTVB) 0.445886 -1.153748
(0.25323) (0.50974)
D(CPI) 0.726540 -0.355133
(0.78209) (1.57434)
D(EX) -1.128808 1.759632
(0.48003) (0.96629)
D(REPO_RATE) 0.846437 -0.890626
(0.56328) (1.13387)
3 Cointegrating Equation(s): Log likelihood 175.9402 Normalized cointegrating coefficients (standard error in parentheses)
LTVE LTVB CPI EX REPO_RATE
1.000000 0.000000 0.000000 0.629532 -0.266146
(0.15816) (0.10907)
0.000000 1.000000 0.000000 0.213179 -0.170948
(0.06399) (0.04413)
0.000000 0.000000 1.000000 0.924534 -1.767521
(0.47250) (0.32584)
Adjustment coefficients (standard error in parentheses)
D(LTVE) -0.431253 0.753237 0.068738
(0.23980) (0.47249) (0.02966)
D(LTVB) 0.337575 -1.010019 0.024231
(0.25755) (0.50746) (0.03185)
D(CPI) 0.938219 -0.636032 -0.221249
(0.80590) (1.58788) (0.09967)
D(EX) -1.265278 1.940728 0.173833
(0.49420) (0.97374) (0.06112)
D(REPO_RATE) 0.510640 -0.445021 -0.029503
(0.56084) (1.10503) (0.06936)
4 Cointegrating Equation(s): Log likelihood 181.2155 Normalized cointegrating coefficients (standard error in parentheses)
LTVE LTVB CPI EX REPO_RATE
1.000000 0.000000 0.000000 0.000000 -0.294869
(0.18568)
0.000000 1.000000 0.000000 0.000000 -0.180674
(0.06394)
0.000000 0.000000 1.000000 0.000000 -1.809703
(0.33985)
0.000000 0.000000 0.000000 1.000000 0.045626
(0.27999)
p
Adjustment coefficients (standard error in parentheses)
D(LTVE) -0.409512 0.761852 0.061129 -0.055727
(0.23636) (0.46468) (0.02970) (0.03552)
D(LTVB) 0.360061 -1.001110 0.016361 0.010951
(0.25417) (0.49968) (0.03194) (0.03820)
D(CPI) 1.031563 -0.599045 -0.253920 0.214584
(0.78572) (1.54471) (0.09873) (0.11809)
D(EX) -1.231066 1.954285 0.161859 -0.235259
(0.49056) (0.96442) (0.06164) (0.07373)
D(REPO_RATE) 0.509349 -0.445533 -0.029051 0.199815
(0.56213) (1.10513) (0.07063) (0.08448)
APPENDIX 2 (B): VECTOR ERROR CORRECTION ESTIMATES RESULTS
Vector Error Correction Estimates
Date: 11/25/12 Time: 16:45
Sample (adjusted): 2000M04 2008M09
Included observations: 102 after adjustments
Standard errors in ( ) & t-statistics in [ ] Cointegrating Eq: CointEq1 LTVE(-1) 1.000000
LTVB(-1) -1.924150
(0.19745)
[-9.74492]
CPI(-1) -0.066981
(0.02439)
[-2.74590]
REPO_RATE(-1) 0.248390
(0.03421)
[ 7.26094]
EX(-1) 0.091507
(0.03515)
[ 2.60307]
C 12.36897
Error Correction: D(LTVE) D(LTVB) D(CPI) D(REPO_RATE
) D(EX) CointEq1 -0.169552 0.133414 0.179899 -0.159538 -0.410531
(0.06547) (0.06884) (0.22905) (0.14889) (0.13334)
[-2.58987] [ 1.93806] [ 0.78542] [-1.07152] [-3.07879]
D(LTVE(-1)) -0.566801 -0.241133 0.076475 0.334343 0.053443
(0.10865) (0.11424) (0.38012) (0.24709) (0.22129)
[-5.21691] [-2.11072] [ 0.20119] [ 1.35312] [ 0.24151]
D(LTVE(-2)) -0.286935 0.099749 0.009422 0.416437 -0.333047
(0.10797) (0.11353) (0.37776) (0.24556) (0.21991)
[-2.65749] [ 0.87859] [ 0.02494] [ 1.69590] [-1.51444]
D(LTVB(-1)) -0.077134 -0.308189 -0.278455 -0.571464 -0.453657
q
(0.11635) (0.12234) (0.40708) (0.26461) (0.23698)
[-0.66294] [-2.51903] [-0.68404] [-2.15962] [-1.91432]
D(LTVB(-2)) -0.150706 -0.417298 0.048015 -0.200672 0.103572
(0.10192) (0.10717) (0.35658) (0.23179) (0.20758)
[-1.47869] [-3.89390] [ 0.13465] [-0.86576] [ 0.49894]
D(CPI(-1)) -0.015151 0.017160 0.308081 -0.022764 0.032083
(0.03101) (0.03261) (0.10850) (0.07053) (0.06316)
[-0.48857] [ 0.52625] [ 2.83957] [-0.32277] [ 0.50795]
D(CPI(-2)) -0.033615 -0.037664 0.031067 0.134076 -0.045617
(0.02639) (0.02775) (0.09234) (0.06002) (0.05376)
[-1.27363] [-1.35713] [ 0.33644] [ 2.23370] [-0.84859]
D(REPO_RATE(-1)) 0.043475 -0.070920 0.687696 0.207163 0.151722
(0.04840) (0.05090) (0.16935) (0.11009) (0.09859)
[ 0.89814] [-1.39337] [ 4.06073] [ 1.88185] [ 1.53892]
D(REPO_RATE(-2)) 0.097935 0.038626 -0.176054 0.309107 0.112729
(0.05293) (0.05565) (0.18517) (0.12037) (0.10780)
[ 1.85038] [ 0.69406] [-0.95075] [ 2.56800] [ 1.04573]
D(EX(-1)) -0.047319 0.104928 0.425561 0.116667 0.295955
(0.05041) (0.05301) (0.17637) (0.11465) (0.10267)
[-0.93868] [ 1.97954] [ 2.41290] [ 1.01763] [ 2.88247]
D(EX(-2)) -0.058617 -0.041947 0.277263 -0.036180 -0.184307
(0.05460) (0.05741) (0.19103) (0.12418) (0.11121)
[-1.07356] [-0.73063] [ 1.45142] [-0.29136] [-1.65731]
C 0.039443 0.016767 0.046703 -0.017196 0.023031
(0.01586) (0.01668) (0.05550) (0.03608) (0.03231)
[ 2.48631] [ 1.00518] [ 0.84144] [-0.47662] [ 0.71279] R-squared 0.391796 0.442605 0.469896 0.312542 0.232733
Adj. R-squared 0.317460 0.374479 0.405105 0.228519 0.138956
Sum sq. resids 2.118894 2.342765 25.93670 10.95938 8.790039
S.E. equation 0.153438 0.161340 0.536829 0.348957 0.312517
F-statistic 5.270608 6.496854 7.252540 3.719729 2.481774
Log likelihood 52.84628 47.72396 -74.89672 -30.96211 -19.71269
Akaike AIC -0.800908 -0.700470 1.703857 0.842394 0.621818
Schwarz SC -0.492087 -0.391649 2.012678 1.151215 0.930638
Mean dependent 0.017092 0.007510 0.095098 0.002451 0.015564
S.D. dependent 0.185725 0.203996 0.696011 0.397291 0.336792 Determinant resid covariance (dof adj.) 1.20E-06
Determinant resid covariance 6.40E-07
Log likelihood 3.664688
Akaike information criterion 1.202653
Schwarz criterion 2.875430
APPENDIX 2 (C): CORRELATION MATRIX
LTVE LTVB CPI EX REPO_RATE
LTVE 1.000000 0.507006 0.078737 0.132340 -0.007257
LTVB 0.507006 1.000000 -0.067628 0.274193 -0.110124
CPI 0.078737 -0.067628 1.000000 -0.107889 0.313409
r
EX 0.132340 0.274193 -0.107889 1.000000 0.023161
REPO_RATE -0.007257 -0.110124 0.313409 0.023161 1.000000
APPENDIX 2 (D): VARIANCE DECOMPOSITION
Varian
ce Decomposition
of LTVE:
Period S.E. LTVE LTVB CPI EX REPO_RATE 1 0.166167 100.0000 0.000000 0.000000 0.000000 0.000000
2 0.189942 96.43353 2.592698 0.009722 0.862521 0.101533
3 0.219346 96.45695 1.945879 0.007356 0.654850 0.934964
4 0.241303 96.22804 1.720619 0.050048 0.764361 1.236931
5 0.260257 95.72932 1.506836 0.096658 0.766673 1.900508
6 0.277486 95.16913 1.346774 0.158650 0.781072 2.544378
7 0.292829 94.46839 1.233768 0.219997 0.800355 3.277493
8 0.306932 93.67975 1.147922 0.271018 0.821696 4.079617
9 0.319900 92.81116 1.086257 0.308046 0.856445 4.938096
10 0.331928 91.86920 1.040486 0.329170 0.907858 5.853290
11 0.343139 90.86189 1.005182 0.335955 0.983131 6.813842
12 0.353637 89.79186 0.975299 0.331441 1.088818 7.812583
13 0.363514 88.66131 0.946910 0.319608 1.231730 8.840437
14 0.372847 87.47070 0.917568 0.304736 1.418504 9.888491
15 0.381713 86.21982 0.886240 0.290987 1.654979 10.94798
16 0.390179 84.90843 0.853248 0.282147 1.945910 12.01027
17 0.398309 83.53684 0.819990 0.281475 2.294563 13.06714
18 0.406161 82.10644 0.788606 0.291621 2.702536 14.11080
19 0.413785 80.62003 0.761633 0.314610 3.169676 15.13406
20 0.421228 79.08196 0.741683 0.351855 3.694134 16.13037
21 0.428528 77.49811 0.731192 0.404193 4.272531 17.09397
22 0.435716 75.87574 0.732225 0.471947 4.900196 18.01989
23 0.442819 74.22318 0.746368 0.554989 5.571456 18.90401
24 0.449856 72.54950 0.774677 0.652817 6.279939 19.74306
25 0.456840 70.86420 0.817684 0.764626 7.018876 20.53462
26 0.463781 69.17681 0.875436 0.889382 7.781372 21.27700
27 0.470683 67.49664 0.947564 1.025884 8.560632 21.96928
28 0.477548 65.83253 1.033362 1.172825 9.350153 22.61113
29 0.484376 64.19261 1.131865 1.328846 10.14386 23.20283
30 0.491162 62.58423 1.241930 1.492569 10.93619 23.74508
31 0.497901 61.01386 1.362303 1.662640 11.72217 24.23902
32 0.504588 59.48706 1.491678 1.837747 12.49742 24.68609
33 0.511215 58.00848 1.628742 2.016643 13.25815 25.08799
34 0.517774 56.58188 1.772212 2.198161 14.00116 25.44659
35 0.524258 55.21023 1.920860 2.381216 14.72377 25.76392
36 0.530658 53.89573 2.073530 2.564814 15.42380 26.04212 Varian
ce Decomposition
of LTVB:
Period S.E. LTVE LTVB CPI EX REPO_RATE 1 0.171771 25.70556 74.29444 0.000000 0.000000 0.000000
s
2 0.193215 21.36735 73.93183 0.131130 4.107501 0.462192
3 0.207407 26.32337 67.25455 0.135530 5.652448 0.634094
4 0.214974 27.52703 65.28008 0.329061 5.771881 1.091946
5 0.220940 29.47009 62.54666 0.574745 6.038821 1.369688
6 0.226206 31.16876 60.06233 0.918218 6.088150 1.762546
7 0.230905 32.69558 57.82369 1.286243 6.139256 2.055225
8 0.235420 34.14390 55.72933 1.661887 6.184324 2.280564
9 0.239717 35.47307 53.82858 2.027946 6.242087 2.428318
10 0.243867 36.71586 52.08631 2.371714 6.320657 2.505455
11 0.247877 37.87085 50.49743 2.687609 6.416413 2.527705
12 0.251756 38.94234 49.05173 2.971530 6.525043 2.509353
13 0.255502 39.93398 47.74069 3.221969 6.638413 2.464956
14 0.259107 40.84942 46.55667 3.438822 6.748189 2.406901
15 0.262563 41.69392 45.49102 3.623081 6.846665 2.345311
16 0.265856 42.47322 44.53456 3.776547 6.927640 2.288030
17 0.268977 43.19338 43.67731 3.901530 6.986934 2.240849
18 0.271917 43.86017 42.90891 4.000646 7.022427 2.207848
19 0.274671 44.47871 42.21899 4.076636 7.033922 2.191741
20 0.277239 45.05317 41.59756 4.132239 7.022825 2.194204
21 0.279624 45.58672 41.03527 4.170106 6.991767 2.216139
22 0.281834 46.08155 40.52359 4.192739 6.944228 2.257894
23 0.283878 46.53896 40.05497 4.202463 6.884186 2.319424
24 0.285768 46.95956 39.62280 4.201414 6.815825 2.400401
25 0.287519 47.34344 39.22142 4.191539 6.743300 2.500301
26 0.289145 47.69034 38.84604 4.174600 6.670568 2.618451
27 0.290660 47.99987 38.49262 4.152190 6.601261 2.754067
28 0.292079 48.27156 38.15782 4.125736 6.538614 2.906275
29 0.293415 48.50506 37.83888 4.096520 6.485420 3.074119
30 0.294682 48.70019 37.53353 4.065683 6.444019 3.256574
31 0.295891 48.85698 37.23992 4.034238 6.416303 3.452553
32 0.297053 48.97572 36.95655 4.003079 6.403739 3.660909
33 0.298176 49.05698 36.68219 3.972988 6.407396 3.880445
34 0.299270 49.10159 36.41586 3.944642 6.427981 4.109920
35 0.300341 49.11068 36.15677 3.918621 6.465877 4.348056
36 0.301395 49.08557 35.90429 3.895413 6.521180 4.593553 Varian
ce Decomposition of CPI:
Period S.E. LTVE LTVB CPI EX REPO_RATE 1 0.520100 0.619956 1.556872 97.82317 0.000000 0.000000
2 0.927330 0.735020 2.719261 85.58439 3.037228 7.924102
3 1.291688 1.665459 3.026714 75.34155 6.556628 13.40965
4 1.594066 2.876372 2.387366 68.56358 9.457903 16.71478
5 1.843638 4.249020 1.798898 63.35810 12.27936 18.31462
6 2.056326 5.771694 1.519048 58.89426 14.89616 18.91883
7 2.242367 7.320222 1.587189 54.96735 17.21699 18.90825
8 2.408803 8.840548 1.937983 51.48659 19.20698 18.52790
9 2.559764 10.29025 2.480409 48.42184 20.85661 17.95089
10 2.697645 11.65075 3.126525 45.74676 22.18989 17.28608
11 2.823905 12.91890 3.808002 43.42894 23.24323 16.60094
12 2.939516 14.09948 4.477892 41.43091 24.05783 15.93389
13 3.045244 15.20153 5.107254 39.71295 24.67348 15.30479
14 3.141772 16.23500 5.680692 38.23645 25.12562 14.72224
15 3.229756 17.20928 6.191965 36.96581 25.44461 14.18832
16 3.309840 18.13256 6.640644 35.86940 25.65573 13.70167
17 3.382655 19.01150 7.029700 34.91981 25.77974 13.25926
18 3.448812 19.85137 7.363882 34.09370 25.83358 12.85747
19 3.508892 20.65617 7.648684 33.37140 25.83105 12.49269
t
20 3.563445 21.42886 7.889713 32.73645 25.78337 12.16160
21 3.612981 22.17154 8.092328 32.17515 25.69969 11.86129
22 3.657972 22.88564 8.261452 31.67611 25.58752 11.58928
23 3.698850 23.57211 8.401491 31.22985 25.45305 11.34351
24 3.736010 24.23147 8.516327 30.82853 25.30139 11.12228
25 3.769812 24.86400 8.609336 30.46560 25.13685 10.92422
26 3.800582 25.46977 8.683435 30.13561 24.96301 10.74817
27 3.828617 26.04871 8.741133 29.83401 24.78295 10.59319
28 3.854187 26.60065 8.784588 29.55699 24.59930 10.45847
29 3.877537 27.12541 8.815653 29.30132 24.41432 10.34330
30 3.898891 27.62277 8.835928 29.06429 24.22997 10.24704
31 3.918454 28.09253 8.846801 28.84360 24.04799 10.16908
32 3.936415 28.53451 8.849484 28.63729 23.86990 10.10882
33 3.952944 28.94859 8.845045 28.44368 23.69703 10.06566
34 3.968200 29.33469 8.834431 28.26134 23.53056 10.03898
35 3.982329 29.69282 8.818492 28.08903 23.37153 10.02813
36 3.995464 30.02304 8.797997 27.92567 23.22085 10.03245 Varian
ce Decomposition of EX:
Period S.E. LTVE LTVB CPI EX REPO_RATE 1 0.309337 1.751396 5.772772 0.797583 91.67825 0.000000
2 0.487616 1.941277 8.246926 0.382786 88.95944 0.469572
3 0.639801 1.621993 14.24124 0.724086 82.86600 0.546690
4 0.776171 1.335574 19.01623 1.163033 77.98948 0.495690
5 0.894087 1.154617 22.58602 1.584098 74.18481 0.490462
6 0.993380 1.022274 25.26101 1.952392 71.24004 0.524279
7 1.075196 0.930105 27.21148 2.265767 68.99788 0.594764
8 1.141482 0.864739 28.62259 2.533986 67.27536 0.703325
9 1.194522 0.818661 29.61563 2.765958 65.95114 0.848608
10 1.236644 0.786828 30.28746 2.969741 64.92629 1.029683
11 1.270020 0.765719 30.71338 3.151459 64.12556 1.243875
12 1.296557 0.753045 30.95306 3.315713 63.49101 1.487176
13 1.317858 0.747311 31.05438 3.465951 62.97802 1.754346
14 1.335214 0.747652 31.05534 3.604781 62.55299 2.039237
15 1.349638 0.753663 30.98568 3.734239 62.19112 2.335302
16 1.361901 0.765283 30.86810 3.855955 61.87463 2.636039
17 1.372577 0.782676 30.71952 3.971254 61.59115 2.935400
18 1.382089 0.806141 30.55221 4.081214 61.33236 3.228080
19 1.390739 0.836024 30.37485 4.186691 61.09275 3.509685
20 1.398747 0.872654 30.19351 4.288341 60.86871 3.776792
21 1.406267 0.916294 30.01232 4.386636 60.65782 4.026926
22 1.413407 0.967108 29.83416 4.481881 60.45838 4.258476
23 1.420245 1.025141 29.66097 4.574246 60.26905 4.470585
24 1.426831 1.090320 29.49413 4.663786 60.08874 4.663018
25 1.433200 1.162457 29.33457 4.750477 59.91646 4.836036
26 1.439370 1.241261 29.18289 4.834239 59.75133 4.990279
27 1.445355 1.326355 29.03949 4.914962 59.59253 5.126661
28 1.451156 1.417295 28.90454 4.992526 59.43936 5.246278
29 1.456772 1.513588 28.77805 5.066815 59.29121 5.350334
30 1.462201 1.614709 28.65989 5.137731 59.14759 5.440085
31 1.467435 1.720115 28.54982 5.205198 59.00808 5.516790
32 1.472469 1.829256 28.44752 5.269166 58.87238 5.581681
33 1.477297 1.941589 28.35260 5.329610 58.74027 5.635939
34 1.481913 2.056580 28.26462 5.386534 58.61159 5.680678
35 1.486315 2.173710 28.18313 5.439965 58.48625 5.716940
36 1.490500 2.292483 28.10769 5.489950 58.36419 5.745689
u
Variance
Decomposition
of REPO_RATE:
Period S.E. LTVE LTVB CPI EX REPO_RATE 1 0.351584 0.005266 1.525066 9.112890 0.711292 88.64549
2 0.535758 0.018747 4.627905 12.08232 4.249391 79.02164
3 0.698313 0.294746 4.502954 14.15578 6.861770 74.18475
4 0.836022 0.529425 3.628027 15.57925 9.843410 70.41989
5 0.959932 0.990282 2.824613 16.41447 13.19690 66.57373
6 1.074170 1.525230 2.272035 16.90028 16.47350 62.82895
7 1.181566 2.116836 2.054586 17.12531 19.61044 59.09284
8 1.283953 2.735344 2.154172 17.17439 22.45392 55.48218
9 1.382001 3.353582 2.506128 17.11119 24.94942 52.07968
10 1.475926 3.960774 3.031891 16.98169 27.08131 48.94433
11 1.565626 4.549538 3.660729 16.81917 28.86420 46.10636
12 1.650909 5.118409 4.335015 16.64550 30.33165 43.56943
13 1.731592 5.668425 5.013014 16.47446 31.52356 41.32054
14 1.807558 6.201961 5.666674 16.31393 32.48085 39.33658
15 1.878773 6.721869 6.278864 16.16785 33.24150 37.58992
16 1.945288 7.230886 6.840511 16.03760 33.83904 36.05197
17 2.007224 7.731395 7.348128 15.92299 34.30214 34.69535
18 2.064757 8.225308 7.801921 15.82294 34.65482 33.49501
19 2.118102 8.714054 8.204374 15.73593 34.91695 32.42870
20 2.167497 9.198615 8.559256 15.66024 35.10480 31.47709
21 2.213192 9.679580 8.870943 15.59416 35.23169 30.62363
22 2.255436 10.15721 9.143969 15.53608 35.30846 29.85428
23 2.294476 10.63150 9.382744 15.48454 35.34393 29.15729
24 2.330545 11.10224 9.591390 15.43823 35.34531 28.52283
25 2.363865 11.56904 9.773654 15.39604 35.31850 27.94277
26 2.394641 12.03143 9.932872 15.35700 35.26833 27.41037
27 2.423066 12.48881 10.07197 15.32032 35.19879 26.92011
28 2.449314 12.94055 10.19349 15.28532 35.11321 26.46743
29 2.473548 13.38599 10.29961 15.25147 35.01434 26.04860
30 2.495914 13.82442 10.39219 15.21833 34.90453 25.66053
31 2.516550 14.25517 10.47282 15.18555 34.78576 25.30070
32 2.535581 14.67753 10.54284 15.15287 34.65975 24.96701
33 2.553122 15.09086 10.60339 15.12008 34.52800 24.65768
34 2.569281 15.49449 10.65543 15.08704 34.39180 24.37124
35 2.584157 15.88780 10.69979 15.05365 34.25234 24.10641
36 2.597842 16.27022 10.73719 15.01984 34.11065 23.86210 Choles
ky Ordering: LTVE
LTVB CPI EX REPO_RATE
APPENDIX 2 (E): VEC RESIDUAL SERIAL CORRELATION TESTS
VAR Residual Serial Correlation LM Tests
Null Hypothesis: no serial correlation at lag
v
order h
Date: 11/23/12 Time: 18:20
Sample: 2000M01 2008M09
Included observations: 103 Lags LM-Stat Prob 1 53.28229 0.0008
2 53.15055 0.0009
3 29.66303 0.2372
4 31.14683 0.1842
5 47.21199 0.0046
6 40.80048 0.0241
7 48.52079 0.0032
8 27.20319 0.3458
9 20.10973 0.7410
10 33.34088 0.1228
11 25.11960 0.4557
12 60.92937 0.0001
Probs from chi-square with 25 df.
APPENDIX 2 (F): VEC RESIDUAL NOMARLITY TESTS
VAR Residual Normality Tests
Orthogonalization: Cholesky (Lutkepohl)
Null Hypothesis: residuals are multivariate normal
Date: 11/23/12 Time: 18:21
Sample: 2000M01 2008M09
Included observations: 103
Component Skewness Chi-sq df Prob. 1 -0.332627 1.899335 1 0.1682
2 -0.565507 5.489866 1 0.0191
3 0.183242 0.576418 1 0.4477
4 1.344581 31.03559 1 0.0000
5 -0.361074 2.238094 1 0.1346 Joint 41.23930 5 0.0000
Component Kurtosis Chi-sq df Prob. 1 3.055021 0.012992 1 0.9093
2 3.619451 1.646798 1 0.1994
3 3.310619 0.414078 1 0.5199
4 9.125989 161.0565 1 0.0000
5 3.772321 2.559895 1 0.1096 Joint 165.6903 5 0.0000
Component Jarque-Bera df Prob. 1 1.912327 2 0.3844
2 7.136664 2 0.0282
3 0.990497 2 0.6094
4 192.0921 2 0.0000
w
5 4.797989 2 0.0908 Joint 206.9296 10 0.0000
APPENDIX 2 (G): VEC Residual Heteroskedasticity Test: No Cross Terms (only levels
and squares)
VAR Residual Heteroskedasticity Tests: No Cross Terms (only levels and squares)
Date: 11/23/12 Time: 18:22
Sample: 2000M01 2008M09
Included observations: 103
Joint test: Chi-sq df Prob. 393.8830 300 0.0002
Individual components: Dependent R-squared F(20,82) Prob. Chi-sq(20) Prob. res1*res1 0.306791 1.814519 0.0323 31.59944 0.0478
res2*res2 0.186027 0.937019 0.5439 19.16073 0.5114
res3*res3 0.204321 1.052833 0.4139 21.04508 0.3945
res4*res4 0.223557 1.180492 0.2921 23.02639 0.2875
res5*res5 0.415862 2.918891 0.0003 42.83380 0.0022
res2*res1 0.284893 1.633410 0.0641 29.34401 0.0812
res3*res1 0.160414 0.783356 0.7255 16.52259 0.6837
res3*res2 0.306295 1.810294 0.0328 31.54840 0.0484
res4*res1 0.184686 0.928736 0.5537 19.02264 0.5204
res4*res2 0.268733 1.506706 0.1014 27.67948 0.1172
res4*res3 0.285222 1.636045 0.0635 29.37785 0.0806
res5*res1 0.163650 0.802252 0.7038 16.85592 0.6623
res5*res2 0.297120 1.733147 0.0441 30.60340 0.0606
res5*res3 0.219011 1.149757 0.3190 22.55818 0.3110
res5*res4 0.390809 2.630238 0.0012 40.25333 0.0046
APPENDIX 2 (H): VEC Granger Causality/Block Exogeneity Wald Tests
VEC Granger Causality/Block Exogeneity Wald Tests
Date: 01/08/13 Time: 23:21
Sample: 2000M01 2008M09
Included observations: 96
Dependent variable: D(LTVB) Excluded Chi-sq df Prob. D(LTVE) 19.71070 8 0.0115
x
D(CPI) 7.048626 8 0.5314
D(EX) 8.411279 8 0.3944 D(REPO_RATE
) 3.151539 8 0.9245 All 43.89770 32 0.0784
Dependent variable: D(LTVE) Excluded Chi-sq df Prob. D(LTVB) 11.55715 8 0.1721
D(CPI) 5.706552 8 0.6801
D(EX) 15.96401 8 0.0429 D(REPO_RATE
) 3.818527 8 0.8731 All 35.61606 32 0.3020
Dependent variable: D(CPI) Excluded Chi-sq df Prob. D(LTVB) 7.333530 8 0.5011
D(LTVE) 7.251330 8 0.5098
D(EX) 21.81468 8 0.0053 D(REPO_RATE
) 22.43475 8 0.0042 All 65.54814 32 0.0004
Dependent variable: D(EX) Excluded Chi-sq df Prob. D(LTVB) 6.796846 8 0.5587
D(LTVE) 21.12215 8 0.0068
D(CPI) 10.28095 8 0.2459 D(REPO_RATE
) 17.72412 8 0.0234 All 53.59525 32 0.0097
Dependent variable: D(REPO_RATE) Excluded Chi-sq df Prob. D(LTVB) 14.31626 8 0.0739
D(LTVE) 7.063080 8 0.5298
D(CPI) 17.50869 8 0.0252
D(EX) 10.15057 8 0.2546 All 49.35917 32 0.0257
APPENDICES FOR MODEL 3
y
APPENDIX 3 (A): JOHANSEN COINTEGRATION TEST RESULTS
Date: 11/23/12 Time: 17:57
Sample (adjusted): 2000M03 2008M09
Included observations: 103 after adjustments
Trend assumption: Linear deterministic trend
Series: FIPE FIPB CPI EX REPO_RATE
Lags interval (in first differences): 1 to 1
Unrestricted Cointegration Rank Test (Trace) Hypothesized Trace 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None * 0.425192 109.9001 69.81889 0.0000
At most 1 * 0.272009 52.86698 47.85613 0.0157
At most 2 0.130991 20.16796 29.79707 0.4115
At most 3 0.034563 5.706519 15.49471 0.7299
At most 4 0.020025 2.083523 3.841466 0.1489 Trace test indicates 2 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegration Rank Test (Maximum Eigenvalue) Hypothesized Max-Eigen 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None * 0.425192 57.03316 33.87687 0.0000
At most 1 * 0.272009 32.69902 27.58434 0.0101
At most 2 0.130991 14.46144 21.13162 0.3284
At most 3 0.034563 3.622996 14.26460 0.8968
At most 4 0.020025 2.083523 3.841466 0.1489 Max-eigenvalue test indicates 2 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegrating Coefficients (normalized by b'*S11*b=I): FIPE FIPB CPI EX REPO_RATE
-1.84E-10 0.000276 -0.112302 -0.047533 0.014994
2.33E-10 0.000138 0.168508 0.310160 -0.026894
1.69E-10 5.68E-05 -0.330976 -0.509817 0.832247
7.48E-11 7.53E-06 -0.320388 0.593813 -0.017935
-2.21E-11 -4.95E-06 -0.132691 0.403274 0.384418
Unrestricted Adjustment Coefficients (alpha): D(FIPE) 1.08E+09 -1.23E+09 -8.99E+08 -1.55E+08 62937793
D(FIPB) -3378.192 -833.6743 -557.2697 143.8143 150.4892
D(CPI) 0.071395 -0.056945 0.021201 0.092493 -0.006180
D(EX) 0.010129 -0.054765 0.014014 -0.016293 -0.038303
D(REPO_RATE) 0.002011 0.089650 -0.086758 0.031561 -0.018499
1 Cointegrating Equation(s): Log likelihood -3569.334
z
Normalized cointegrating coefficients (standard error in parentheses)
FIPE FIPB CPI EX REPO_RATE
1.000000 -1499026. 6.11E+08 2.59E+08 -81576281
(201728.) (3.3E+08) (6.0E+08) (5.5E+08)
Adjustment coefficients (standard error in parentheses)
D(FIPE) -0.198937
(0.07071)
D(FIPB) 6.21E-07
(8.8E-08)
D(CPI) -1.31E-11
(9.8E-12)
D(EX) -1.86E-12
(5.7E-12)
D(REPO_RATE) -3.70E-13
(6.8E-12)
2 Cointegrating Equation(s): Log likelihood -3552.984 Normalized cointegrating coefficients (standard error in parentheses)
FIPE FIPB CPI EX REPO_RATE
1.000000 0.000000 6.91E+08 1.03E+09 -1.06E+08
(2.8E+08) (5.1E+08) (4.5E+08)
0.000000 1.000000 53.53359 512.9401 -16.17211
(235.082) (429.392) (383.499)
Adjustment coefficients (standard error in parentheses)
D(FIPE) -0.485797 128749.8
(0.10795) (112013.)
D(FIPB) 4.27E-07 -1.045571
(1.4E-07) (0.14539)
D(CPI) -2.64E-11 1.18E-05
(1.6E-11) (1.6E-05)
D(EX) -1.46E-11 -4.75E-06
(9.1E-12) (9.5E-06)
D(REPO_RATE) 2.05E-11 1.29E-05
(1.1E-11) (1.1E-05)
3 Cointegrating Equation(s): Log likelihood -3545.754 Normalized cointegrating coefficients (standard error in parentheses)
FIPE FIPB CPI EX REPO_RATE
1.000000 0.000000 0.000000 -65025088 1.20E+09
(5.2E+08) (3.3E+08)
0.000000 1.000000 0.000000 428.3280 84.83632
(422.613) (271.984)
0.000000 0.000000 1.000000 1.580542 -1.886823
(0.58557) (0.37686)
Adjustment coefficients (standard error in parentheses)
D(FIPE) -0.637951 77741.62 -31518141
(0.12026) (110218.) (1.4E+08)
D(FIPB) 3.32E-07 -1.077206 423.3412
(1.6E-07) (0.14676) (181.809)
D(CPI) -2.28E-11 1.30E-05 -0.024631
(1.8E-11) (1.7E-05) (0.02050)
D(EX) -1.23E-11 -3.96E-06 -0.015004
(1.0E-11) (9.6E-06) (0.01191)
D(REPO_RATE) 5.84E-12 7.97E-06 0.043596
(1.2E-11) (1.1E-05) (0.01357)
aa
4 Cointegrating Equation(s): Log likelihood -3543.942 Normalized cointegrating coefficients (standard error in parentheses)
FIPE FIPB CPI EX REPO_RATE
1.000000 0.000000 0.000000 0.000000 1.16E+09
(2.6E+08)
0.000000 1.000000 0.000000 0.000000 361.8884
(239.597)
0.000000 0.000000 1.000000 0.000000 -0.864493
(0.40065)
0.000000 0.000000 0.000000 1.000000 -0.646822
(0.25321)
Adjustment coefficients (standard error in parentheses)
D(FIPE) -0.649560 76573.03 18229062 -67236428
(0.12298) (110138.) (1.8E+08) (3.0E+08)
D(FIPB) 3.43E-07 -1.076123 377.2648 271.5081
(1.6E-07) (0.14673) (235.664) (394.904)
D(CPI) -1.59E-11 1.37E-05 -0.054264 0.023059
(1.8E-11) (1.6E-05) (0.02616) (0.04383)
D(EX) -1.35E-11 -4.08E-06 -0.009784 -0.034287
(1.1E-11) (9.6E-06) (0.01542) (0.02584)
D(REPO_RATE) 8.20E-12 8.21E-06 0.033484 0.090682
(1.2E-11) (1.1E-05) (0.01752) (0.02936)
APPENDIX 3 (B): VECTOR ERROR CORRECTION ESTIMATES RESULTS
Vector Error Correction Estimates
Date: 11/26/12 Time: 09:31
Sample (adjusted): 2000M04 2008M09
Included observations: 102 after adjustments
Standard errors in ( ) & t-statistics in [ ] Cointegrating Eq: CointEq1 FIPB(-1) 1.000000
FIPE(-1) -3.51E-08
(1.5E-07)
[-0.24026]
CPI(-1) -320.7489
(194.501)
[-1.64908]
EX(-1) 270.2657
(336.806)
[ 0.80244]
REPO_RATE(-1) 443.6477
(338.927)
[ 1.30898]
bb
C -4359.517
Error Correction: D(FIPB) D(FIPE) D(CPI) D(EX) D(REPO_RATE
) CointEq1 -1.217566 -208430.0 1.03E-05 -1.17E-05 -4.31E-07
(0.18315) (145343.) (2.1E-05) (1.3E-05) (1.4E-05)
[-6.64788] [-1.43405] [ 0.48783] [-0.93522] [-0.03044]
D(FIPB(-1)) 0.253443 76083.99 -3.91E-06 -3.66E-06 2.51E-06
(0.14592) (115799.) (1.7E-05) (1.0E-05) (1.1E-05)
[ 1.73684] [ 0.65703] [-0.23229] [-0.36699] [ 0.22245]
D(FIPB(-2)) 0.188217 206795.6 1.13E-06 4.35E-06 7.77E-06
(0.10273) (81521.9) (1.2E-05) (7.0E-06) (7.9E-06)
[ 1.83219] [ 2.53669] [ 0.09499] [ 0.61861] [ 0.97910]
D(FIPE(-1)) 3.82E-08 -0.420644 1.21E-11 -7.59E-12 1.94E-12
(1.2E-07) (0.09502) (1.4E-11) (8.2E-12) (9.2E-12)
[ 0.31914] [-4.42694] [ 0.87464] [-0.92715] [ 0.20947]
D(FIPE(-2)) 2.11E-07 -0.377509 -3.94E-12 -1.30E-11 2.37E-12
(1.2E-07) (0.09293) (1.4E-11) (8.0E-12) (9.0E-12)
[ 1.79914] [-4.06213] [-0.29183] [-1.62798] [ 0.26144]
D(CPI(-1)) 116.4110 1.38E+09 0.322495 0.035542 -0.007188
(942.080) (7.5E+08) (0.10863) (0.06445) (0.07278)
[ 0.12357] [ 1.84986] [ 2.96865] [ 0.55145] [-0.09877]
D(CPI(-2)) -931.6331 -5.12E+08 0.001072 -0.029078 0.131014
(825.499) (6.6E+08) (0.09519) (0.05648) (0.06377)
[-1.12857] [-0.78190] [ 0.01127] [-0.51487] [ 2.05446]
D(EX(-1)) 928.0342 -1.42E+08 0.383846 0.317080 0.180590
(1586.92) (1.3E+09) (0.18299) (0.10857) (0.12259)
[ 0.58480] [-0.11273] [ 2.09762] [ 2.92054] [ 1.47311]
D(EX(-2)) 1741.220 -2.07E+08 0.233390 -0.001438 -0.048448
(1649.88) (1.3E+09) (0.19025) (0.11288) (0.12746)
[ 1.05536] [-0.15776] [ 1.22674] [-0.01274] [-0.38012]
D(REPO_RATE(-1)) -381.0731 -1.60E+09 0.719637 0.038426 0.160950
(1355.67) (1.1E+09) (0.15633) (0.09275) (0.10473)
[-0.28110] [-1.48572] [ 4.60344] [ 0.41430] [ 1.53685]
D(REPO_RATE(-2)) 2403.801 -1.38E+09 -0.105279 -0.034806 0.244665
(1493.59) (1.2E+09) (0.17223) (0.10218) (0.11538)
[ 1.60941] [-1.16089] [-0.61127] [-0.34063] [ 2.12050]
C 93.52569 -2.03E+08 0.049324 0.009455 -0.012496
(478.270) (3.8E+08) (0.05515) (0.03272) (0.03695)
[ 0.19555] [-0.53578] [ 0.89434] [ 0.28897] [-0.33822] R-squared 0.571871 0.359597 0.460817 0.189427 0.257318
Adj. R-squared 0.519544 0.281325 0.394917 0.090356 0.166546
Sum sq. resids 1.98E+09 1.25E+21 26.38091 9.286176 11.83975
S.E. equation 4695.119 3.73E+09 0.541407 0.321216 0.362702
F-statistic 10.92881 4.594227 6.992651 1.912045 2.834768
Log likelihood -1000.685 -2386.283 -75.76279 -22.51298 -34.90270
Akaike AIC 19.85657 47.02516 1.720839 0.676725 0.919661
Schwarz SC 20.16539 47.33398 2.029659 0.985546 1.228481
Mean dependent 36.61765 -71433059 0.095098 0.015564 0.002451
cc
S.D. dependent 6773.602 4.40E+09 0.696011 0.336792 0.397291 Determinant resid covariance (dof adj.) 9.46E+23
Determinant resid covariance 5.06E+23
Log likelihood -3507.257
Akaike information criterion 70.04426
Schwarz criterion 71.71704
APPENDIX 3 (C): CORRELATION MATRIX
FIPE FIPB CPI EX REPO_RATE
FIPE 1.000000 0.061057 -0.145131 0.062735 0.026414
FIPB 0.061057 1.000000 0.020835 -0.236797 0.065341
CPI -0.145131 0.020835 1.000000 -0.126693 0.354302
EX 0.062735 -0.236797 -0.126693 1.000000 -0.008529
REPO_RATE 0.026414 0.065341 0.354302 -0.008529 1.000000
APPENDIX 3 (D): VARIANCE DECOMPOSITION
Varian
ce Decomposition of FIPE:
Period S.E. FIPE FIPB CPI EX REPO_RATE 1 3.64E+09 100.0000 0.000000 0.000000 0.000000 0.000000
2 3.87E+09 97.83869 0.520863 0.013704 0.131122 1.495618
3 3.95E+09 94.92661 2.842248 0.053096 0.288779 1.889272
4 3.98E+09 93.51808 3.029982 0.177482 0.397105 2.877353
5 4.01E+09 92.48854 2.992607 0.415991 0.570854 3.532009
6 4.04E+09 91.23063 2.955401 0.760045 0.878085 4.175838
7 4.07E+09 89.80349 2.912485 1.186078 1.321523 4.776427
8 4.10E+09 88.26832 2.866009 1.649586 1.873153 5.342937
9 4.14E+09 86.71190 2.817172 2.120526 2.515885 5.834514
10 4.18E+09 85.16800 2.769418 2.579362 3.230426 6.252789
11 4.22E+09 83.66551 2.725578 3.013636 3.992287 6.602990
12 4.25E+09 82.22839 2.686184 3.415287 4.777963 6.892176
13 4.29E+09 80.87301 2.651258 3.780354 5.568833 7.126545
14 4.32E+09 79.60750 2.621012 4.107731 6.350297 7.313466
15 4.36E+09 78.43531 2.595512 4.398097 7.110806 7.460280
16 4.39E+09 77.35704 2.574469 4.653200 7.841603 7.573690
17 4.42E+09 76.37107 2.557460 4.875438 8.536480 7.659556
18 4.45E+09 75.47408 2.544058 5.067563 9.191329 7.722971
19 4.47E+09 74.66170 2.533839 5.232459 9.803706 7.768299
20 4.49E+09 73.92891 2.526376 5.373002 10.37249 7.799223
21 4.52E+09 73.27035 2.521261 5.491964 10.89761 7.818811
22 4.53E+09 72.68052 2.518117 5.591958 11.37980 7.829603
23 4.55E+09 72.15393 2.516604 5.675407 11.82037 7.833681
24 4.57E+09 71.68521 2.516418 5.744525 12.22111 7.832738
25 4.58E+09 71.26919 2.517292 5.801312 12.58407 7.828141
26 4.60E+09 70.90095 2.518992 5.847563 12.91151 7.820980
27 4.61E+09 70.57588 2.521318 5.884867 13.20582 7.812120
dd
28 4.62E+09 70.28963 2.524100 5.914630 13.46940 7.802235
29 4.63E+09 70.03821 2.527193 5.938080 13.70468 7.791844
30 4.63E+09 69.81790 2.530479 5.956284 13.91400 7.781342
31 4.64E+09 69.62532 2.533859 5.970165 14.09964 7.771018
32 4.65E+09 69.45736 2.537254 5.980515 14.26379 7.761081
33 4.65E+09 69.31120 2.540603 5.988008 14.40851 7.751676
34 4.66E+09 69.18430 2.543856 5.993215 14.53574 7.742892
35 4.66E+09 69.07435 2.546977 5.996616 14.64727 7.734781
36 4.66E+09 68.97929 2.549941 5.998612 14.74480 7.727364 Varian
ce Decomposition of FIPB:
Period S.E. FIPE FIPB CPI EX REPO_RATE 1 4852.743 0.372795 99.62720 0.000000 0.000000 0.000000
2 4917.563 0.493015 97.02467 0.383851 0.440593 1.657869
3 5051.832 4.022200 93.06264 0.670235 0.623254 1.621669
4 5061.383 4.135780 92.74205 0.823643 0.681544 1.616984
5 5070.050 4.142836 92.62929 0.882550 0.720114 1.625207
6 5073.640 4.162073 92.50449 0.912266 0.755488 1.665685
7 5075.475 4.161405 92.44586 0.925020 0.772175 1.695541
8 5076.808 4.165522 92.40078 0.928532 0.775779 1.729386
9 5078.096 4.173888 92.35397 0.928514 0.775503 1.768123
10 5079.413 4.180135 92.30619 0.928117 0.776009 1.809545
11 5080.730 4.182812 92.25860 0.928709 0.780043 1.849840
12 5082.130 4.183503 92.20789 0.930863 0.789376 1.888371
13 5083.649 4.182850 92.15279 0.934746 0.804737 1.924882
14 5085.287 4.181115 92.09343 0.940263 0.826167 1.959021
15 5087.033 4.178620 92.03034 0.947198 0.853372 1.990470
16 5088.872 4.175686 91.96406 0.955285 0.885816 2.019150
17 5090.785 4.172548 91.89533 0.964240 0.922776 2.045110
18 5092.749 4.169382 91.82496 0.973780 0.963432 2.068445
19 5094.742 4.166314 91.75381 0.983646 1.006948 2.089283
20 5096.741 4.163430 91.68268 0.993605 1.052502 2.107779
21 5098.725 4.160787 91.61233 1.003461 1.099320 2.124105
22 5100.675 4.158413 91.54340 1.013050 1.146690 2.138443
23 5102.573 4.156321 91.47648 1.022245 1.193979 2.150971
24 5104.407 4.154510 91.41204 1.030949 1.240637 2.161867
25 5106.165 4.152968 91.35044 1.039092 1.286199 2.171299
26 5107.838 4.151677 91.29198 1.046633 1.330283 2.179428
27 5109.420 4.150617 91.23685 1.053549 1.372586 2.186402
28 5110.907 4.149762 91.18516 1.059835 1.412877 2.192361
29 5112.297 4.149090 91.13699 1.065503 1.450992 2.197430
30 5113.589 4.148575 91.09230 1.070573 1.486824 2.201723
31 5114.784 4.148194 91.05107 1.075075 1.520317 2.205345
32 5115.885 4.147927 91.01319 1.079043 1.551458 2.208387
33 5116.894 4.147754 90.97853 1.082518 1.580269 2.210930
34 5117.815 4.147656 90.94695 1.085540 1.606803 2.213048
35 5118.653 4.147620 90.91829 1.088151 1.631134 2.214804
36 5119.412 4.147630 90.89237 1.090391 1.653356 2.216252 Varian
ce Decomposition of CPI:
Period S.E. FIPE FIPB CPI EX REPO_RATE 1 0.538883 2.106312 0.088516 97.80517 0.000000 0.000000
ee
2 0.990441 1.084206 0.307031 89.33163 2.153230 7.123908
3 1.398117 1.359685 0.223842 83.07061 5.274552 10.07131
4 1.754797 1.634982 0.224196 78.33945 8.270809 11.53056
5 2.061335 1.930515 0.195969 74.87408 10.94457 12.05486
6 2.323631 2.177385 0.163984 72.21401 13.26578 12.17884
7 2.547796 2.409147 0.137523 70.09868 15.27296 12.08169
8 2.739461 2.630122 0.119069 68.36271 17.01597 11.87213
9 2.903452 2.841056 0.108186 66.90554 18.53838 11.60683
10 3.043814 3.039984 0.104099 65.66198 19.87317 11.32077
11 3.163924 3.226835 0.105731 64.58824 21.04555 11.03364
12 3.266614 3.401574 0.111875 63.65399 22.07563 10.75693
13 3.354270 3.564117 0.121453 62.83747 22.97984 10.49713
14 3.428927 3.714423 0.133522 62.12239 23.77189 10.25778
15 3.492332 3.852640 0.147255 61.49608 24.46355 10.04047
16 3.545997 3.979055 0.161956 60.94823 25.06519 9.845566
17 3.591243 4.094050 0.177053 60.47020 25.58607 9.672627
18 3.629221 4.198087 0.192091 60.05452 26.03458 9.520724
19 3.660946 4.291689 0.206715 59.69455 26.41843 9.388611
20 3.687307 4.375431 0.220656 59.38435 26.74470 9.274856
21 3.709085 4.449922 0.233720 59.11849 27.01995 9.177925
22 3.726967 4.515794 0.245778 58.89197 27.25020 9.096251
23 3.741552 4.573689 0.256753 58.70023 27.44105 9.028271
24 3.753364 4.624255 0.266614 58.53905 27.59762 8.972464
25 3.762856 4.668131 0.275362 58.40454 27.72459 8.927373
26 3.770421 4.705944 0.283030 58.29316 27.82624 8.891618
27 3.776399 4.738299 0.289670 58.20169 27.90643 8.863914
28 3.781076 4.765777 0.295350 58.12720 27.96860 8.843077
29 3.784700 4.788929 0.300147 58.06706 28.01584 8.828024
30 3.787477 4.808271 0.304147 58.01896 28.05085 8.817780
31 3.789580 4.824283 0.307435 57.98081 28.07600 8.811476
32 3.791155 4.837407 0.310098 57.95082 28.09333 8.808345
33 3.792320 4.848048 0.312221 57.92744 28.10458 8.807716
34 3.793172 4.856568 0.313882 57.90931 28.11123 8.809014
35 3.793789 4.863296 0.315154 57.89531 28.11449 8.811747
36 3.794235 4.868522 0.316106 57.88450 28.11537 8.815502 Varian
ce Decomposition of EX:
Period S.E. FIPE FIPB CPI EX REPO_RATE 1 0.315095 0.393573 5.811827 1.246525 92.54807 0.000000
2 0.528429 0.239848 10.87724 0.753008 87.88563 0.244275
3 0.683291 0.597118 10.19257 0.589125 87.94046 0.680729
4 0.804569 1.203730 9.388623 0.504797 87.98771 0.915144
5 0.902483 1.594199 8.974613 0.456443 87.88344 1.091300
6 0.982601 1.831709 8.807057 0.428597 87.70578 1.226855
7 1.048510 1.993956 8.692451 0.414486 87.56375 1.335359
8 1.103330 2.114675 8.599604 0.409037 87.45676 1.419924
9 1.149415 2.201001 8.530685 0.408919 87.37128 1.488117
10 1.188476 2.261635 8.481382 0.412067 87.29984 1.545076
11 1.221794 2.304781 8.443653 0.417193 87.24040 1.593976
12 1.250372 2.335934 8.413211 0.423406 87.19074 1.636706
13 1.275009 2.358361 8.388233 0.430077 87.14867 1.674664
14 1.296342 2.374353 8.367483 0.436777 87.11253 1.708861
15 1.314883 2.385632 8.349886 0.443221 87.08125 1.740012
16 1.331053 2.393462 8.334667 0.449222 87.05403 1.768617
17 1.345198 2.398749 8.321309 0.454669 87.03023 1.795045
18 1.357606 2.402160 8.309442 0.459500 87.00932 1.819573
19 1.368519 2.404192 8.298788 0.463695 86.99091 1.842412
ff
20 1.378139 2.405219 8.289135 0.467260 86.97466 1.863726
21 1.386638 2.405524 8.280324 0.470223 86.96028 1.883644
22 1.394162 2.405318 8.272234 0.472623 86.94755 1.902273
23 1.400836 2.404760 8.264770 0.474508 86.93626 1.919700
24 1.406766 2.403971 8.257860 0.475931 86.92624 1.936001
25 1.412044 2.403040 8.251444 0.476947 86.91733 1.951242
26 1.416750 2.402034 8.245474 0.477610 86.90940 1.965482
27 1.420951 2.401002 8.239912 0.477972 86.90234 1.978774
28 1.424707 2.399979 8.234725 0.478083 86.89604 1.991170
29 1.428069 2.398990 8.229885 0.477986 86.89042 2.002715
30 1.431083 2.398052 8.225369 0.477723 86.88540 2.013456
31 1.433787 2.397177 8.221154 0.477331 86.88090 2.023435
32 1.436216 2.396370 8.217222 0.476842 86.87687 2.032694
33 1.438401 2.395634 8.213556 0.476282 86.87325 2.041273
34 1.440367 2.394971 8.210140 0.475676 86.87000 2.049209
35 1.442138 2.394378 8.206960 0.475044 86.86708 2.056542
36 1.443734 2.393853 8.204001 0.474402 86.86444 2.063307 Varian
ce Decomposition
of REPO_RATE:
Period S.E. FIPE FIPB CPI EX REPO_RATE 1 0.360406 0.069771 0.407650 12.97517 0.222938 86.32448
2 0.543096 0.259201 0.248726 18.37143 2.462653 78.65799
3 0.705774 0.431595 0.292120 21.71596 5.771289 71.78904
4 0.852627 0.388829 0.257949 24.02829 9.554527 65.77041
5 0.988759 0.309754 0.207816 25.50819 13.36945 60.60479
6 1.115109 0.243578 0.163777 26.43460 17.01494 56.14310
7 1.232592 0.214520 0.137257 26.96995 20.39534 52.28293
8 1.341737 0.226667 0.129691 27.23546 23.48333 48.92485
9 1.443014 0.274282 0.139343 27.31266 26.27872 45.99500
10 1.536803 0.349455 0.163317 27.25957 28.79627 43.43139
11 1.623453 0.444333 0.198296 27.11717 31.05713 41.18307
12 1.703294 0.552173 0.241356 26.91477 33.08458 39.20712
13 1.776655 0.667469 0.290042 26.67349 34.90153 37.46747
14 1.843864 0.785973 0.342350 26.40860 36.52951 35.93356
15 1.905253 0.904501 0.396671 26.13125 37.98821 34.57937
16 1.961155 1.020722 0.451749 25.84953 39.29536 33.38264
17 2.011906 1.132971 0.506618 25.56935 40.46679 32.32427
18 2.057841 1.240102 0.560542 25.29497 41.51660 31.38779
19 2.099292 1.341363 0.612969 25.02945 42.45729 30.55893
20 2.136585 1.436307 0.663492 24.77495 43.29995 29.82530
21 2.170038 1.524710 0.711820 24.53292 44.05446 29.17609
22 2.199960 1.606523 0.757756 24.30429 44.72960 28.60182
23 2.226646 1.681821 0.801171 24.08960 45.33324 28.09416
24 2.250380 1.750775 0.841998 23.88907 45.87242 27.64574
25 2.271428 1.813622 0.880213 23.70266 46.35347 27.25004
26 2.290044 1.870648 0.915831 23.53017 46.78209 26.90127
27 2.306464 1.922172 0.948895 23.37124 47.16343 26.59426
28 2.320908 1.968533 0.979471 23.22541 47.50217 26.32442
29 2.333583 2.010079 1.007643 23.09212 47.80256 26.08760
30 2.344675 2.047164 1.033507 22.97076 48.06844 25.88012
31 2.354358 2.080137 1.057173 22.86069 48.30333 25.69867
32 2.362790 2.109342 1.078754 22.76122 48.51042 25.54027
33 2.370116 2.135109 1.098372 22.67165 48.69261 25.40226
34 2.376465 2.157756 1.116148 22.59129 48.85257 25.28224
35 2.381956 2.177585 1.132205 22.51944 48.99268 25.17809
gg
36 2.386693 2.194881 1.146666 22.45544 49.11514 25.08788 Choles
ky Ordering: FIPE
FIPB CPI EX REPO_RATE
APPENDIX 3 (E): VEC RESIDUAL SERIAL CORRELATION TESTS
VAR Residual Serial Correlation LM Tests Null Hypothesis: no serial correlation at lag order h
Date: 11/23/12 Time: 18:06
Sample: 2000M01 2008M09
Included observations: 103 Lags LM-Stat Prob 1 34.30509 0.1015
2 30.39165 0.2100
3 34.93970 0.0893
4 24.33588 0.5000
5 44.09866 0.0106
6 18.04360 0.8405
7 42.08529 0.0176
8 17.87225 0.8478
9 11.84008 0.9878
10 34.31101 0.1014
11 28.48091 0.2862
12 32.45309 0.1453
Probs from chi-square with 25 df.
APPENDIX 3 (F): VEC RESIDUAL NOMARLITY TESTS
VAR Residual Normality Tests
Orthogonalization: Cholesky (Lutkepohl)
Null Hypothesis: residuals are multivariate normal
Date: 11/23/12 Time: 18:07
Sample: 2000M01 2008M09
Included observations: 103
Component Skewness Chi-sq df Prob. 1 0.160933 0.444609 1 0.5049
2 0.614532 6.482976 1 0.0109
3 0.078976 0.107072 1 0.7435
4 1.547326 41.10074 1 0.0000
5 -0.565195 5.483816 1 0.0192 Joint 53.61921 5 0.0000
hh
Component Kurtosis Chi-sq df Prob. 1 3.374317 0.601318 1 0.4381
2 5.205363 20.87306 1 0.0000
3 3.025978 0.002896 1 0.9571
4 10.43035 236.9435 1 0.0000
5 4.361170 7.951526 1 0.0048 Joint 266.3723 5 0.0000
Component Jarque-Bera df Prob. 1 1.045927 2 0.5928
2 27.35604 2 0.0000
3 0.109968 2 0.9465
4 278.0443 2 0.0000
5 13.43534 2 0.0012 Joint 319.9915 10 0.0000
APPENDIX 3 (G): VEC Residual Heteroskedasticity Test: No Cross Terms (only levels
and squares)
VAR Residual Heteroskedasticity Tests: No Cross Terms (only levels and squares)
Date: 11/23/12 Time: 18:05
Sample: 2000M01 2008M09
Included observations: 103
Joint test: Chi-sq df Prob. 332.9060 300 0.0927
Individual components: Dependent R-squared F(20,82) Prob. Chi-sq(20) Prob. res1*res1 0.303816 1.789250 0.0356 31.29308 0.0514
res2*res2 0.241871 1.308052 0.1980 24.91273 0.2048
res3*res3 0.178785 0.892601 0.5967 18.41482 0.5601
res4*res4 0.209492 1.086538 0.3792 21.57766 0.3639
res5*res5 0.314411 1.880259 0.0250 32.38433 0.0394
res2*res1 0.113491 0.524884 0.9480 11.68961 0.9263
res3*res1 0.324703 1.971403 0.0174 33.44441 0.0301
res3*res2 0.135274 0.641384 0.8694 13.93318 0.8339
res4*res1 0.143562 0.687270 0.8276 14.78688 0.7885
res4*res2 0.222458 1.173026 0.2985 22.91316 0.2931
res4*res3 0.164004 0.804332 0.7014 16.89246 0.6599
res5*res1 0.202477 1.040916 0.4265 20.85511 0.4057
res5*res2 0.217692 1.140906 0.3270 22.42232 0.3180
res5*res3 0.221717 1.168007 0.3029 22.83685 0.2969
res5*res4 0.411127 2.862453 0.0004 42.34609 0.0025
ii
APPENDIX 3 (H): VEC Granger Causality/Block Exogeneity Wald Tests
VEC Granger Causality/Block Exogeneity Wald Tests
Date: 01/08/13 Time: 23:30
Sample: 2000M01 2008M09
Included observations: 103
Dependent variable: D(FIPB) Excluded Chi-sq df Prob. D(FIPE) 10.69201 1 0.0011
D(CPI) 1.348858 1 0.2455
D(EX) 1.059104 1 0.3034 D(REPO_RATE
) 2.564613 1 0.1093 All 15.43349 4 0.0039
Dependent variable: D(FIPE) Excluded Chi-sq df Prob. D(FIPB) 10.77905 1 0.0010
D(CPI) 1.384126 1 0.2394
D(EX) 0.133137 1 0.7152 D(REPO_RATE
) 2.998070 1 0.0834 All 15.13236 4 0.0044
Dependent variable: D(CPI) Excluded Chi-sq df Prob. D(FIPB) 0.408557 1 0.5227
D(FIPE) 1.826771 1 0.1765
D(EX) 8.880271 1 0.0029 D(REPO_RATE
) 22.69533 1 0.0000 All 34.10753 4 0.0000
Dependent variable: D(EX) Excluded Chi-sq df Prob. D(FIPB) 4.058931 1 0.0439
D(FIPE) 0.130402 1 0.7180
D(CPI) 0.058294 1 0.8092 D(REPO_RATE
) 0.042629 1 0.8364 All 5.360015 4 0.2523
Dependent variable: D(REPO_RATE)
jj
Excluded Chi-sq df Prob. D(FIPB) 0.101635 1 0.7499
D(FIPE) 0.032409 1 0.8571
D(CPI) 4.079137 1 0.0434
D(EX) 1.632201 1 0.2014 All 5.994006 4 0.1996
