THE IMPACT OF MACROECONOMIC VARIABLES TOWARD CREDIT DEFAULT

SWAP SPREADS IN ASIA AND EUROPE

Created by:

RIZMA YANIKA CHUSNA

1110081100018

MANAGEMENT DEPARTMENT

INTERNATIONAL PROGRAM

FACULTY OF ECONOMIC AND BUSINESS

STATE ISLAMIC UNIVERSITY SYARIF HIDAYATULLAH JAKARTA

1435 H/2014

v

CURRICULUM VITAE

Personal Identities

Name : Rizma Yanika Chusna

Gender : Female

Place of Birth : Curup

Date of Birth : January, 12th

1992

Address : Kemang Ifi Graha blok F9/14, Jati Asih, Bekasi.

Phone/Mobile : 085966656322

E-mail Address : rizmamama@yahoo.com

Formal Education

College : UIN Syarif Hidayatullah Jakarta

Senior High School : SMAN 48 Jakarta

Junior High School : SMPN 9 Bekasi

Elementary School : SDI Al-Azhar 9 Kemang Pratama

Kindergarten : TKI Al-Azhar Kemang Pratama

vi

ABSTRACT

This study empirically examines the impact of the interaction between

macroeconomic variables and the Credit Default Swap (CDS) spreads with 5 years

maturity in Asia and Europe. The applied macroeconomic variables in this research

are: GDP growth, inflation, unemployment, import/GDP, and current account

balance. This study uses annually data from 2009-2013. Using panel data regression

method with the Random Effect Model, the finding can be summarized as follows: (i)

the inflation, unemployment, and current account balance variable is significantly

affect the CDS spreads in Asia and Europe , while the GDP growth and import/GDP

are not significantly affect the CDS spreads in Asia and Europe. (ii) inflation and

unemployment have positive sign, indicates that the higher inflation and

unemployment leads to the higher Credit Default Swap spreads, meanwhile, current

account balance has a negative sign, indicates that the higher current account balance

leads to the lower Credit Default Swap spreads. (iii) Based on the research, the result

shows that 26.42% of the independent variables have influence the dependent

variable that is the Credit Default Swap spreads, while the rest of 73.58% influenced

by the other variables which are not contained in this research.

Keywords: Macroeconomic variables, CDS spreads, panel data regression, random

effect model.

vii

ABSTRAK

Penelitian ini secara empiris bertujuan untuk menguji dampak dari interaksi

antara variabel ekonomi makro dan Credit Default Swap (CDS) Spreads dengan 5

tenor tahun di Asia dan Eropa. Variabel makroekonomi yang digunakan dalam

penelitian ini adalah: pertumbuhan PDB, inflasi, pengangguran, impor/GDP, dan

neraca transaksi berjalan. Penelitian ini menggunakan data tahunan pada periode

2009-2013. Dengan menggunakan metode regresi data panel dengan Random Effect

Model, hasil dari temuan dapat dijabarkan sebagai berikut: (i) variabel inflasi,

pengangguran, dan neraca transaksi berjalan secara signifikan mempengaruhi CDS

spreads di Asia dan Eropa, sedangkan variabel pertumbuhan PDB dan impor / GDP

tidak berpengaruh secara signifikan terhadap CDS spreads di Asia dan Eropa. (ii)

variabel inflasi dan pengangguran memiliki tanda positif, mendandakan bahwa

peningkatan tingkat inflasi dan pengangguran menyebabkan peningkatan pada Credit

Default Swap spreads, sementara itu, neraca transaksi berjalan memiliki tanda

negatif, mendandakan bahwa peningkatan pada neraca transaksi berjalan

menyebabkan penurunan pada Credit Default Swap spreads. (iii) Hasil dari penelitian

menunjukkan bahwa sebesar 26.42% variabel independen dapat menjelaskan variabel

dependen yaitu Credit Default Swap spreads, sedangkan sisanya 73.58% dijelaskan

oleh variabel-variabel lain yang tidak terdapat dalam penelitian.

Kata Kunci: Variabel Makroekonomi, CDS spreads, Regresi data panel, random

effect model.

viii

PREFACE

Assalamu’alaikum Wr. Wb.

All praise to Allah SWT, because of His blessings and grace the writer can finish

this thesis as one of the requirements in accomplishing the bachelor degree.

The writer also want to thank profusely to all of the parties who had sacrificing

their time to give motivation and advice to the writer, thus, this thesis can be finished

properly. The writer realizes that without the support from the several parties, this

thesis cannot be finished properly. In this change, the writer would like to say thank

to:

1. My Parents Achmad Chusnun and Ratna Sari, who always support me

morally and materially. Thank you for always encourage me to finish my

thesis. Thank you for your love and always pray for your daughter. Sorry I

have not been able to repay all that you give for me on the last 22 years.

2. Prof. Dr. Abdul Hamid, MS., as the Dean of Economic and Business Faculty

UIN Syarif Hidayatullah.

3. Prof. Dr. Ahmad Rodoni as my supervisor I, who always encourage, guide

and motivate me. Thank you for the time that you have spared to help me to

finish this thesis. Thank you for always being nice when I ask question sir.

May Allah gives His blessing for you.

4. Titi Dewi Warninda, SE., M.Si as my supervisor II, who always encourage,

guide and motivate me. Thank you for the time that you have spared to help

me to finish this thesis. May Allah gives His blessing for you.

5. Mas Arif Gunawan and Kak Joan Natasya from Bloomberg TV. Thank you

for helping me on data collection process. Without your help, this thesis

cannot be complete.

ix

6. Pak Raffi and his friends from “Biro Kebijakan Fiskal” Indonesian Ministry

of Finance, who also help me in data collection process. Thank you so much,

without your help, this thesis cannot be complete.

7. All of the staff in Economic and Business Faculty on 3rd

floors. Especially pak

Bonyx who always nicely help me to prepare the documents that I need to

make the thesis.

8. Elfa Halim Ahmad and Annisa Gustiarawanti who always support me and

accompany me in order to get the data that I need to finish this thesis.

Sometimes we are fight, sometime we are arguing, sometimes we are dissent,

sometimes we are complaining about one and each other. Nevertheless,

behind that, there were million of memorable moments that we have passed

together. You are all the best that I ever had. Thank you for all joy and the

sorrow that all we share together for the last 4 years.

9. My friends in Management International 2010 who always nice to me: Afif,

Andro, Rahim, Erbi, Ali, Aufa, Futri and Diena. Thank you for being good

friends.

10. Little brother Muhammad Rizal Fahim who always support me.

11. All of the friends who always help me during the assistance Shelly, Anjar,

Aris, Vae, and Umi

The writer realizes that this thesis is far from perfection due to the limited

knowledge of writer. All of the suggestions and criticism are welcomed in order to

make this thesis better. I hope, this thesis will be useful for the other researcher or

reader.

Jakarta, November ,2014

The Writer

Rizma Yanika Chusna

x

TABLE OF CONTENT

Curriculum Vitae ........................................................................................................... v

Abstract ........................................................................................................................ vi

Abstrak ........................................................................................................................ vii

Preface ........................................................................................................................ viii

Table of Contents .......................................................................................................... x

List of Tables.............................................................................................................. xiv

List of Figures ............................................................................................................. xv

List of Appendix ........................................................................................................ xvi

Chapter I INTRODUCTION

A. Background ..................................................................................... 1

B. Formulation of the Problem ............................................................ 9

C. Research Purposes ........................................................................... 9

D. Research Advantages ......................................................................... 10

Chapter II LITERTURE REVIEW

A. Bond .............................................................................................. 11

1. Characteristic of Bond............................................................. 12

2. Bond Pricing............................................................................ 12

B. Credit Ratings................................................................................ 13

C. Credit Risk and Credit Default ...................................................... 15

D. Credit Default Swap ...................................................................... 16

1. The Purposes of CDS .............................................................. 20

xi

E. Macroeconomic Variables ............................................................ 20

1. GDP Growth Rate ................................................................... 21

2. Inflation ................................................................................... 22

3. Unemployment ........................................................................ 23

4. Import/GDP ............................................................................. 24

5. Current Account Balance ........................................................ 25

F. Previous Research ......................................................................... 26

G. Theoretical Framework...................................................................... 31

H. Hypothesis ..................................................................................... 33

Chapter III RESEARCH METHODOLOGY

A. Scope of the Research ................................................................... 36

B. Sampling Method ............................................................................... 36

C. Data Collection Method ................................................................ 37

D. Analysis Technique ............................................................................ 38

1. Analysis Method ..................................................................... 38

2. Stationary Test ............................................................................. 38

3. Classic Assumption Test ......................................................... 39

a. Normality Test .................................................................. 39

b. Heteroscedasticity Test ..................................................... 40

c. Multicollinearity Test............................................................ 41

d. Autocorrelation Test.......................................................... 42

4. Panel Data Regression ................................................................. 42

a. Pooled Ordinary Least Square (PLS) ................................ 44

b. Fixed Effect Model (FEM) ................................................... 45

xii

c. Random Effect Model (REM) ........................................... 46

(1) Model Selection on Panel Data ................................... 47

(a) Chow Test ............................................................. 47

(b) Hausman Test............................................................ 48

5. t-test ......................................................................................... 49

6. F-test ........................................................................................ 49

7. Adjusted R2 ............................................................................. 50

E. Variable Operational Research ..................................................... 50

1. Independent Variables ............................................................. 50

a. GDP Growth...................................................................... 50

b. Inflation ............................................................................. 50

c. Unemployment ...................................................................... 51

d. Import/GDP ....................................................................... 51

e. Current Account Balance .................................................. 51

2. Dependent Variable ................................................................. 51

Chapter IV FINDING AND ANALYSIS

A. General Description of Research Object ....................................... 53

1. Credit Default Swap ................................................................ 54

2. Bloomberg LP ......................................................................... 55

B. Data Description ................................................................................. 56

1. Descriptive Statistics ............................................................... 56

a. CDS Spreads .......................................................................... 57

b. GDP Growth...................................................................... 57

c. Inflation .................................................................................. 57

d. Unemployment .................................................................. 57

xiii

e. Import/GDP ............................................................................ 57

f. Current account balance .................................................... 58

2. Data Processing ....................................................................... 66

a. Stationary Test .................................................................. 66

b. Classic Assumption Test ................................................... 69

(1) Normality Test................................................................. 69

(2) Heteroscedasticy Test.................................................. 71

(3) Multicollinearity Test .................................................. 72

(4) Autocorrelation Test.................................................... 73

c. Model Selection in Panel Data Regression......................... 74

(1) Chow Test ................................................................... 75

(2) Hausman Test .............................................................. 76

d. Hypothesis Testing ............................................................ 77

(1) t-Test ................................................................................. 77

(2) F-Test ............................................................................... 82

e. Regression Equation.......................................................... 83

f. The Effect of Macroeconomic Variables in Explaining the

Credit Default Swap spreads ................................................ 85

Chapter V CONCLUSION

A. Conclusion..................................................................................... 86

B. Suggestion ..................................................................................... 87

1. For Investors............................................................................ 87

2. For Academics ........................................................................ 88

3. For Government ...................................................................... 88

xiv

C. The Limits of the Research and Recommendation ...................... 88

REFERENCES ...................................................................................................... 90

xv

LIST OF TABLES

No. Description

1.1 Credit Ratings and Credit Default Swap Spread of Lehman Brothers ............... 3

2.1 Definition of Bond Ratings Classification ...................................................... 14

2.2 Overview Previous Research ............................................................................... 29

3.1 The Difference between Cross Section and Time Series Data ....................... 43

4.1 Descriptive Statistics ............................................................................................. 56

4.2 CDS Spreads .......................................................................................................... 58

4.3 GDP Growth .......................................................................................................... 59

4.4 Inflation .......................................................................................................... 61

4.5 Unemployment Total ............................................................................................ 62

4.6 Import of Goods and Services/GDP ................................................................ 63

4.7 Current Account Balance ...................................................................................... 64

4.8 Stationary Test (at Level) ................................................................................ 67

4.9 Stationary Test (at 1st Difference) ........................................................................ 68

4.10 Stationary Test (at 2nd

Difference) .................................................................. 69

4.11 The Result of White Heteroscedasticity Test .................................................. 71

4.12 The Result of Multicollinearity Test ............................................................... 72

4.13 The Result of Autocorrelation Test using Durbin-Watson Statistic ................ 73

4.14 The Result of Chow Test................................................................................. 75

4.15 The Result of Hausman Test ................................................................................ 76

4.16 The Result of t-Test ......................................................................................... 78

4.17 The Result of F-Test .............................................................................................. 83

xvi

LIST OF FIGURES

No. Description

1.1 Asian CDS Spreads in Basis Points ........................................................................ 5

2.1 Credit Default Swap Mechanism .................................................................... 18

2.2 Theoretical Framework ......................................................................................... 32

4.1 Annual CDS Notionals Outstanding ($) ......................................................... 54

4.2 The Result of Jarque-Bera Test for Normality ................................................ 70

xvii

LIST OF APPENDIX

No. Description

Appendix I List of Countries .................................................................................... 94

Appendix II Descriptive Statistics ........................................................................ 97

Appendix III Stationary Test ....................................................................................... 98

Appendix IV Normality Test ............................................................................... 110

Appendix V Hesteroscedasticity Test ................................................................. 111

Appendix VI Multicollinearity Test .......................................................................... 112

Appendix VII Autocorrelation Test....................................................................... 113

Appendix VIII Chow Test ............................................................................................. 114

Appendix IX Hausman Test ................................................................................. 115

Appendix X The Result of Pooled Least Square ................................................ 116

Appendix XI The Result of Fixed Effect Model ..................................................... 117

Appendix XII The Result of Random Effect Model ............................................. 118

Appendix XIII Partial t-Test ......................................................................................... 120

Appendix XIV Simultaneous F-Test ............................................................................ 121

Appendix XV Adjusted R2 .................................................................................... 122

1

CHAPTER I

INTRODUCTION

A. Research Background

According to Fitch, credit ratings provide an opinion on the relative

ability of an entity to meet financial commitments, such as interest, preferred

dividends, re-payment of principal, insurance claims or counterparty

obligations. Credit ratings have been used to measure the potential of default

or default risk since long time ago. Default risk is the probability that the

interest and principal will not be paid in the promised amounts on the due

dates or will not be paid at all (Ross, et. al, 2010:628). Bond default risk or

credit risk is measured by these global institutions, they are: Moody’s Investor

Services, Standard & Poor Corporation (S&P), and Fitch Investors Service.

These three institutions provide bond ratings on corporate or sovereign bond

based on the evaluation of the probability that the bond issuer will make the

promised payment.

In fact, there was a criticism on bond ratings or credit ratings during the

global financial crisis led by the subprime mortgage crisis in 2008. During

2007-2008 credit ratings remained relatively unchanged. For example,

California’s Orange County and Enron, received high credit ratings until just

before they filed for bankruptcy protection (Flannery, et. al, 2010). The failure

2

of credit rating agency perform the accurate forecast during the subprime

mortgage crisis led the investors to look for the other instruments that can

accurately predict the bond investment.

In recent year, Credit Default Swap (CDS) assessed to be the more

accurate predicted instrument rather than bond ratings. Credit Default Swap is

an insurance contract which pays off in the event of credit event. Credit event

mentioned here can be interpreted as default or bankruptcy (Keown, et. al,

2010:23). The credit default swap (CDS) is the most commonly-used credit

derivative instrument, it has enabled investors to insure against a credit event

such as the default of a reference entity. Reference entity mentioned here is

the bond issuer (Baum and Wan, 2010). Sovereign Credit Default Swap

(SCDS) can be used to protect investors against losses on sovereign debt

arising from so-called credit events such as default or debt restructuring (IMF

2013). The buyers of CDS must pay the periodic premium or spread.

According to Flannery, et. al, (2010) even though the credit ratings

remained relatively unchanged, CDS spreads increased during 2007 and 2008

as information became available showing that the probability of defaults by

financial institutions was increasing. Lehman Brother’s rating is still A until it

declares its bankruptcy in September 2008, but its CDS spread continue to

rise along with the company’s failure in March 2008 until its bankruptcy in

September 2008. The Lehman Brothers ratings before it declare for its

bankruptcy can be seen in table 1.1

3

Table 1.1

Credit Ratings and Credit Default Swap Spread of Lehman Brothers

Date Lehman Brothers

Ratings Spread

1/2/06 A 25

1/1/07 A 21

4/2/07 A 38

7/10/07 A 45

8/17/07 A 150

1/1/08 A 120

3/14/08 A 448

9/12/08 A 702

9/15/08 A 703

Source: Sand, 2012

Table 1.1 shows that the Lehman Brothers ratings is still A even before it

declare for it’s bankruptcy in September 2008. According to Moody’s and

S&P, A rating means that the company has strong capacity to pay interest and

principal. Based on the Moody’s and S&P description Lehman Brothers is

supposed to be far from bankrupt, because it has a strong capacity to pay

interest and principle. In real, in September 2008, Lehman Brothers is declare

for it bankruptcy. In the other hand, Lehman Brothers’s Credit Default Swap

spreads shows the significant change from 120 bps in January, 1st, 2008 to

448 bps in March, 14th

, 2008, and continue to rise on September, 9th

, 2008 to

702 bps. A credit default swap is a bilateral agreement between two parties, a

4

buyer and a seller of credit protection. The premium paid by the buyer of

protection known as CDS spreads. CDS spreads change over time based on

supply and demand for particular CDS contracts. In March 2008, Lehman

Brothers spreads started to rise until September 2008. This trend was occurs

due to the increasing on the CDS demand caused by the loss of investors

confidence toward the Lehman Brother’s future outlook. This was led by the

subprime mortgage crisis where the Lehman Brother is contributed to sell the

large number of subprime mortgage debenture. It proven that Credit Default

Swap is more accurately in predicting the potential of default compare with

the bond rating.

Since the characteristic of modern economy interaction patterns is to have

a high linkage one and each other, so it is possible that one country can

experience an economic uncertainty (crisis) as an impact of external

turbulence. Practically, there are no countries that can isolate their economic

from external shock (Ariefianto and Soepomo, 2011). Thus, when the

subprime mortgage crisis was occur in the US, Asian and European bond

market were also affected by the crisis. This is evidenced by the increasing on

Asian CDS spread during the subprime mortgage in 2008 and the decreasing

on CDS spread during the recovery in the mid of 2009. For the detail, Figure

1.1 will be showing the Asian Credit Default Swap spread during the period

of 2008-2012.

5

Figure 1.1

Asian CDS spread in basis points

Sources: Asian Development Bank (2012)

Macroeconomic factors are important indicators of economic and every

week statistics shown that the macroeconomic factors connected to the market

(Brandorf and Holmberg, 2010). It was proven when the Greece faced the

debt crisis which had spread to the other Eurozone countries such as Ireland

and Portugal. According to bureau Fitch in Brandorf and Holmberg, 2010 “the

credit rating downgrade on Portugal on March 24th

2010 was mostly based on

weak macroeconomic figures such as a budget deficit of 9,3% of GDP”. The

credit rating downgrade in Portugal in 2010 followed by the increasing on its

CDS spreads from 91.61 bps in 2009 move to 500.97 bps in 2010 (Bloomberg

LP).

6

Furthermore, based on preceding illustration, the author is interested to

figure out the relationship between macroeconomic variables and Credit

Default Swap Spread in Asia and Europe. Because of that, “The Impact of

Macroeconomic Variables toward Credit Default Swap Spread in Asia

and Europe” has been chosen by the author as the title of this research. For

conducting this research, the author uses some of macroeconomic variables.

The macroeconomic variables that are used are: GDP growth rate, inflation,

unemployment rate, import/GDP, and current account balance.

The author limits the sample into 9 Asian countries and 5 European

countries. Asian countries are represented by: China, Hong Kong, Indonesia,

Japan, Malaysia, Philippines, South Korea, Thailand, and Vietnam while

European countries are represented by Germany, Ireland, Italy, Portugal, and

Spain. The data that used by the author for conducting this research are the

data from 2009-2013, in order to figure out the impact of macroeconomic

variables on CDS spreads after global crisis or in the post global crisis.

The relationship between macroeconomic factor and CDS spread have

been examined by some of the researchers. As like, Brandorf and Holmberg

(2010) that had conducted a research regarding the macroeconomic variables

toward Credit Default Swap spread in Portugal, Ireland, Italy, Greece, and

Spain. Brandorf and Holmberg use GDP growth, inflation, unemployment,

and sovereign gross debt to represent the macroeconomic variable. In their

research, Brandorf and Holmberg use a multiple regression model. The result

7

shows that unemployment is the most significant variable, while inflation is

the least significant variable that affecting CDS spread in PIIGS countries.

Aini (2012) had conducted a research regarding the impact of interest

rates, stock returns, and implied volatility toward Credit Default Swap spreads

in Indonesia. In her research, Aini was employ government securities (SUN)

with 10 years maturity as a proxy of interest rates, Jakarta Composite Index

(JCI) returns as a proxy of stock returns, and vstoxx index as a proxy of

implied volatility. This research uses multiple regression analysis as the

method. The result shows that interest rates, stock returns and implied

volatility have an impact toward Credit Default Swap spreads in Indonesia.

Implied volatility has the most effect toward CDS spreads in Indonesia

compare with the other independent variable.

The other research had conducted by Ho (2014). His Research relates to

the Long-run Determinants of the Sovereign CDS in Emerging Countries. In

his research, Ho is using 8 emerging countries as sample of the research. The

applied emerging countries are: Brazil, Indonesia Malaysia, Mexico, South

Africa, South Korea, Thailand, and Turkey. In his research, Ho was employ 3

macroeconomic variables which are: the current account, the external debt,

and the international reserves to measure the determinant of sovereign CDS.

The research is using the pooled mean group cointegration approach. The

result shows that in long-run, the current account, the external debt, and the

international reserves variables have a significant effect toward sovereign

8

CDS. The international reserve is the highest relatively to the two other

coefficient. While, in the short-run, the external debt and international

reserves are significant for eight emerging countries. The current account is

not significant for all countries.

What makes the author research different from the previous research is

the sample, variables and the method of the research. Brandorf and Holmberg

(2010) had chosen PIIGS countries as the sample of the research, used GDP

growth, inflation, unemployment, and sovereign gross debt to represent the

macroeconomic variable and using multiple regression analysis as the method

of the research. Aini (2012) had used Indonesia as the sample of the research,

employ interest rates, stock returns, and implied volatility as the independent

variable, and used multiple regression as the method of the research. Ho

(2014) had used 8 emerging countries such as: Brazil, Indonesia Malaysia,

Mexico, South Africa, South Korea, Thailand, and Turkey as the sample of

the research, applied the current account, the external debt, and the

international reserves variables, and using pooled mean group cointegration

method for conducting the research. Meanwhile, the author of this research

had chosen selected Asian and European countries as a sample of the research,

employ GDP growth, inflation, unemployment, import/GDP, and current

account balance as the independent variable, and using panel data regression

method for conducting the research.

9

The researcher is using the countries that located in European contingen,

because, it refers to the previous research that had been conducted by some of

the researcher who is Brandorf and Holmberg (2010) and Sand (2012) that

used some of European countries as a sample of their research. Meanwhile, in

order to distinguish this research from the previous research, the researcher

add the countries that located in Asia as a sample. In order to avoid the

imbalances between Asian and European countries, thus, the researcher not

only using the developing countries in Europe, but also the developed

countries such as Portugal, Ireland, Italy, and Spain as the sample.

B. Formulation of the Problem

1. Is there any impact between GDP growth, inflation, unemployment,

import/GDP, and current account balance partially toward the Credit

Default Swap spreads in Asia and Europe?

2. Is there any effect between GDP growth, inflation, unemployment,

import/GDP, and current account balance simultaneously toward the

Credit Default Swap spreads in Asia and Europe?

C. Research Purposes

The purposes of this research are:

1. To analyze the significances of the GDP growth, inflation, unemployment,

import/GDP, and current account balance partially toward the Credit

Default Swap spreads in Asia and Europe.

10

2. To examine empirically the effect of macroeconomic variables that

represented by GDP growth, inflation, unemployment, import/GDP, and

current account balance toward the Credit Default Swap spreads in Asia

and Europe.

D. Research Advantages

1. For the Researcher

Enrich the author knowledge regarding the financial condition in Asia and

Europe particularly on the derivatives market (bond and Credit Default Swap

spread).

2. For the Investors

As a guidance for the investor who wants to particularly start their investing

activity on sovereign bond to consider the macroeconomic condition that

might affect bond market in a country before decides to do an investment.

3. For the Academics

a. Giving additional insight for the academics.

b. As a reference for the other researchers for conducting the research

especially regarding Credit Default Swap spread.

4. For the Government

As information for government agencies (for instance Indonesian Bank,

Ministry of Finance, etc) relates to the macroeconomic factors which

affect the Credit Default Swap spreads, as a consideration on the decision

making and as a consideration to determine the policy.

11

CHAPTER II

LITERATURE REVIEW

A. Bond

Bond is a security that is issued in connection with a borrowing

agreement. Borrower (the issuer of the bond) sells a bond to the lender for

some amount of cash. The agreement obligates the issuer to make specified

payments to bond holder on specified dates. A typical coupon bond obligates

the issuer to make semiannual payments of the interest to bondholder for the

life of the bond (Bodie, et. al, 2009:446).

Bond is an instrument in which the issuer (borrower) promises to repay to

the lender/investor the amount borrowed (principal) plus interest (coupon)

over some specified period of time. The interest is usually paid at specified

intervals, such as semi-annually or quarterly. When the bond matures, the

investor receives the entire amount invested, or the principal plus coupon

(www.idx.co.id, 2014a).

According to Eun, et. al, (2012:236) A foreign bond issue is one offered

by a foreign borrower to the investors in a national capital market and

denominated in that nation’s currency. For instance, a Swiss MNC issuing

dollar-denominated bonds to the U.S. investors.

12

Bond is a long-term (10 years or more) promissory note issued by a

borrower, promising to pay the owner of the security a predetermined amount

of interest each year (Keown, et. al, 2011:27).

1. Characteristic of Bonds

According to www.idx.co.id (2014b), the characteristics of bond, are:

a. Nominal value (face value) is bond issued or bond principals.

b. Coupon (the interest rate) is the interest value occasionally received by the

bondholders (usually every 3 or 6 months). Bond’s coupon is asserted in

annual percentages.

c. Tenor (maturity) is the date when the bondholders will receive principal

payment of the bond. The maturity date of each bond varies from 365 days

to more than 5 years. A bond close to maturity date has lower risk than a

bond that is far from maturity. It is because a bond that close to the

maturity date is easier to predict.

2. Bond Pricing

According to www.idx.co.id (2014c), bond’s price is measured in

percentages (%) unit that is percentages of its nominal value. There are 3

(three) possibility of the bond’s price offered to the market:

13

a. Par Value = Bond’s price is the same as its nominal value.

For example: A bond with nominal value Rp 50 million sold at price

100%, the bond’s value is 100% x Rp 50 million = Rp 50

million.

b. At Premium = Bond’s price is higher than its nominal value.

For example: A bond with nominal value Rp 50 million sold at price

102%, the bond’s value is 102% x Rp 50 million = Rp 51

million.

c. At discount = Bond’s price is lower than its nominal value.

For example: Bond with nominal value Rp 50 million sold at price 98%,

the bond’s value is 98% x Rp 50 million = Rp 49 million.

B. Credit Ratings

According to Fitch, credit ratings provide an opinion on the relative

ability of an entity to meet financial commitments, such as interest, preferred

dividends, repayment of principal, insurance claims or counterparty

obligations.

According to S&P Credit ratings are forward-looking opinions about

credit risk. Standard & Poor’s credit ratings express the agency’s opinion

about the ability and willingness of an issuer, such as a corporation or state or

city government, to meet its financial obligations in full and on time.

14

Sovereign credit ratings are an assessment of the creditworthiness of a

government’s ability and willingness to make timely payment of the principal

and the interests of its debt (Mellios and Blanc, 2006).

Sovereign credit ratings provided by the three major rating agencies:

Fitch Ratings, Moody’s and Standard & Poors. The three institutions have a

different symbol for determining its grade. Table 2.1 will provide the symbol

of credit ratings and its meaning by S&P and Moody’s

Table 2.1

Definition of Bond Ratings Classification

Moody’s Standard & Poor’s Description

Aaa AAA The highest rating. Capacity to pay

interest and principal is extremely strong.

Aa AA Very strong capacity to pay interest and

principal.

A A Strong capacity to pay interest and

principal. It is somewhat more

susceptible to the adverse effect of

changes in circumstances and economic

condition rather than higher rated

categories.

Baa BBB Adequate capacity to pay interest and

principal. Changing in circumstances and

economic condition could impact the

ability to pay.

Ba BB Less vulnerable to nonpayment than

other speculative issues. However, it

faces major ongoing uncertainties or

exposure to adverse business, financial,

or economic conditions.

B, Caa, Ca B, CCC, CC Extremely speculative and highly

vulnerable to nonpayment.

15

Moody’s Standard & Poor’s Description

C C No interest is being paid.

D D Default. Payments of interest or

repayment of principal is in arrears.

Sources: Bodie, et. al (2009) and Keown, et. al (2011)

Although S&P and Moody’s have a different symbol for representing the

investment grade, actually, the symbols have a similar meaning. BBB or

above (S&P) or Baa and above (Moody’s) are considered as an investment-

grade bonds, whereas lower-rated bonds are classified as speculative-grade or

junk bonds. (Bodie, et. al, 2009:467).

All rating agencies’ issuer credit ratings express a relative ranking of an

issuer’s creditworthiness, which reflects the issuer's overall ability and

willingness to meet its senior, unsecured obligations. It is based on current

information provided by obligors or obtained from other reliable sources.

(Ismailescu and Kazemi, 2009).

C. Credit Risk and Credit Default

The term credit risk commonly refers to the risk of not getting repaid

upon granting some counterpart credit (Brandorf and Holmberg, 2010). Credit

risk has a close relation with credit default. It relates one and each other

because the credit risk is the situation when the investors facing a risk of loss

because of default by the counterparty (Hull, 2009:770).

16

A sovereign default event occurs when a scheduled debt service is not

paid in the specified in the debt contract. Unlike the corporation, on the rare

occasion when the government defaults on its debt, it cannot declare for its

bankruptcy. Under this situations, creditors and the defaulting borrower

generally exchange or restructuring its debt (Ismailescu and Kazemi, 2009).

D. Credit Default Swap

Near the end of 20th

century, a new derivatives instrument called Credit

Default Swap (CDS) was emerge and introduced as a tools to measure the

sovereign risk (Ariefianto and Soepomo, 2011).

A credit default swap is a bilateral agreement between two parties, a

buyer and a seller of credit protection. In its simplest form, the protection

buyer agrees to make periodic payments over a predetermined number of

years (the maturity of the CDS) to the protection seller. In exchange, the

protection seller commits to making a payment to the buyer in the event of

default by a third party (the reference entity). CDS quotes now commonly

relied upon as indicators of investor’s perceptions of credit risk regarding

individual firms and their willingness to bear the risk. (Bomfim, 2005:68).

Sovereign Credit Default Swap (SCDS) can be used to protect investors

against losses on sovereign debt arising from so-called credit events such as

default or debt restructuring (IMF 2013).

17

One of the most important terms in a CDS agreement is the definition of a

credit event. According to Fontana and Scheicher, 2010, the credit event

described by International Swaps and Derivatives Association are (ISDA):

1. Failure to pay principal or coupon when they are due: the failure to pay a

coupon might represent a credit event, although most likely one with a

high recovery.

2. Restructuring is a change in the terms of a debt obligation that is adverse

to creditors, such as a lengthening of the maturity of debt.

3. Repudiation / moratorium is occurs when the reference entity rejects or

challenges the validity of its obligations.

There are some additional credit event added by Bomfim (2005:290) based on

ISDA:

4. Bankruptcy is a situation where the reference entity unable to repay its

debts. This credit event does not apply to CDS written on sovereign

reference entities.

5. Obligation Acceleration occurs when an obligation has become due and

payable earlier than it would have otherwise been.

When the investors or the buyer of protection decide to protect their bond

investment through CDS, they have to pay a periodic payment to the seller of

CDS. The periodic payment called premium or spreads. CDS premium tend to

be paid quarterly, and the most common maturity are three, five, and ten

years, with the five-year maturity being especially active.

18

According to Flannery, et. al, (2010) CDS prices, represent the size of the

premium paid by the buyer of protection and are generally known as CDS

spreads. CDS spreads change over time based on supply and demand for

particular CDS contracts. CDS spreads reflect market participants assessment

of the risk of a default or credit event associated with the underlying

obligation. The CDS premium or spreads are measured in basis points (bps). 1

basis points equals to 0.01%. An illustration of CDS premium payment can be

seen figure 2.1

Figure 2.1

Credit Default Swap mechanisms

Source: Bomfim, 2005

Generally the term of CDS have a similarity with the traditional

insurance product. The protection buyers have to pay the premium or spreads

Protection

Buyer

Protection

Seller

Pay the premium

(quarterly)

()

If reference entity default,

seller pays par for the

defaulted debt

Refrence

entity

19

to the protection seller for secure their bond investment from the potential of

default by the reference entity. If the reference entity facing the default during

the life of the contract, the protection seller will pays the par to the protection

buyers. Meanwhile, if the reference entity is not facing the default during the

life of the contact, so that, the protection buyers will lose their premium or

spread. Unlike the traditional insurance, should not to have any reference

assets to buy the CDS. It generally called naked basis.

In the CDS spreads payment are known the terms of settlement payment.

According to Carboni (2011), the settlement payment is made by the seller

according to the contract settlement option. In credit derivatives, there are two

options contract of the settlement, they are cash settlement and physical

settlement.

In the physical settlement, when a credit event occurs, the buyer delivers

the reference asset to the seller, in return for which the seller pays the face

value (par value) of the delivered asset to the buyer. The contract may specify

a number of alternative assets (called deliverable obligations) that the buyer

can deliver when the default is occur. On the other hand, in the cash

settlement option the contract specifies a predetermined payout value when a

credit event occurs. Generally, the protection seller pays the buyer the

difference between the nominal amount of the default swap and the final

(market) value of the reference asset, determined by the dealer banks. This

last value can be viewed as the recovery value of the asset.

20

1. The purposes of CDS

According to the types of reference entity, the CDS contracts can be

categorized into two groups. The first one is corporate CDS and the other

one is sovereign CDS. The International Monetary Fund (2013) is

mentioned the objectives of Sovereign Credit Default Swap (SCDS):

a. Hedging : The owners of sovereign bond buy SCDS to protect

themselves against losses arising from a default or other credit event

affecting the value of the underlying debt.

b. Speculating : SCDS contracts can be used to buy (or sell) protection

on a naked basis, (the buyer of SCDS does not have any reference

assets) to express a negative or positive opinion about the credit

outlook of the issuer of the underlying bonds.

c. Basis trading : SCDS are used get a profit from differences between

SCDS and the underlying bond obligations price by taking offsetting

positions in the two (“basis trading”). This strategy is based on the

principle that CDS can be used to replicate the cash flows of

underlying obligations.

E. Macroeconomic Variables

Macroeconomic is the branch of economic that examines the economic

behavior of aggregates (income, employment, output) in the national scale.

(Case, et. al, 2009:32). There are a lot of macroeconomic variables that can be

used to measure the condition of a country. In this study, the author

21

emphasize on five macroeconomic variables to figure out the effect of

macroeconomic variables toward CDS spreads in Asia and Europe. The

following explanation will describe the macroeconomic variables used by the

author:

1. GDP Growth Rate

Gross Domestic Product (GDP) is the total market value of all final

goods and services produced within a given period by factors of

production located within a country. (Case, et. al, 2009:129). Recent

levels of country’s GDP may be used to measure recent economic growth

(Madura, 2010:480).

According to Frank and Bernanke (2009:439), there are two ways to

measure GDP, they are:

a. Real GDP : a measure of GDP in which the quantities

produced are valued at the price in a base year rather

than at current pricees; real GDP measures the actual

physical volume of production.

b. Nominal GDP : a measure of GDP in which the quantities produced

are valued at current-year prices; nominal GDP

measures the current dollar value of production.

The GDP growth rate measures how fast the economy is growing. It

does this by comparing one quarter of the country's economic output

(Gross Domestic Product) to the last. The GDP growth rate is driven by

22

the four components of GDP. The most important driver of GDP growth

is personal consumption, which includes retail sales. GDP growth is also

driven by business investment, which includes construction and inventory

levels. Government spending is another driver of growth, and is

sometimes necessary to jumpstart the economy after a recession. Last, but

not least, are exports and imports. Exports drive growth, but increases in

imports have a negative impact (Amadeo, 2014)

Annual percentage growth rate of GDP at market prices based on

constant local currency. GDP is the sum of gross value added by all

resident producers in the economy plus any product taxes and minus any

subsidies not included in the value of the products. It is calculated without

making deductions for depreciation of fabricated assets or for depletion

and degradation of natural resources (www.worldbank.org, 2014a).

The research relates to the relationship between GDP growth and

Credit Default Swap spreads had conducted by Brandrof and Homberlg

(2010) indicate that CDS spreads decrease in GDP growth rate.

2. Inflation

Inflation is an increase in the price level, but not all price increases

constitutes to inflation. When prices of some goods are rising and the

others are falling, these are relative price changes, while inflation occurs

when there is an increase on overall price level (Case, et. al, 2009:154).

23

According to www.worldbank.org (2014b), inflation can be

determined by either Consumer Price Index or GDP deflator. Inflation as

measured by the Consumer Price Index reflects the annual percentage

change in the cost to the average consumer of acquiring a basket of goods

and services. Meanwhile the inflation as measured by the annual growth

rate of the GDP deflator shows the rate of price change in the economy as

a whole. This research is using the inflation measured by the Consumer

Price Index data.

The research relates to the relationship between inflation and the

Credit Default Swap spreads had conducted by Sand (2012), the result

indicates that the inflation variable is significantly affect the CDS spreads.

3. Unemployment

In the United States, defining and measuring the unemployment is the

responsibility of Bearu of Labor Statistics (BLS). According to Frank and

Bernanke (2009:449), a person is unemployed if he or she did not work

during the preceding week but made some effort to find a work, in the past

four weeks. To find the unemployment rate, the first step that should have

to calculated is the size of labor force. Labor force is defined as the total

number of employed and unemployed people in the economy. People who

are in the age of 16 or older who are in the school or disable is not counted

as unemployed, thus, they are not affect the unemployment rate. The

unemployment rate is defined as the number of unemployed people

24

divided by the labor force. In general, a high rate of unemployment rate

indicates that the economy of a country is performing poorly.

Unemployment rate is the ratio of the number of people employed to

the total number of of people in the labor force (Case, et. al, 2009:148).

Unemployment refers to the share of the labor force that is without work

but available for and seeking for a job (www.worldbank.org, 2014c).

The research relates to the relationship between unemployment and

Credit Default Swap spreads (CDS) had conducted by Brandrof and

Holmberg (2010), the result shows that the CDS spreads increase in

unemployment.

4. Import/GDP

Imports of goods and services represent the value of all goods and

other market services received from the rest of the world. They include the

value of merchandise, freight, insurance, transport, travel, royalties,

license fees, and other services, such as communication, construction,

financial, information, business, personal, and government services. They

exclude compensation of employees and investment income (formerly

called factor services) and transfer payments (www.worldbank.org,

2014d).

25

The relationship between import/GDP and CDS spreads had

conducted by Sand (2012), the result shows that the CDS spreads is

increase along with the increasing on the import/GDP variable.

5. Current Account Balance

The balance of payments consist of the current account and capital

account. In this study I focus on the current account. Current account

represents a summary of the flow of funds between one specified country

and all other countries due to the purchases of goods or services, or the

provision of income on financial assets (Madura, 2010:27). Madura

(2010:27-28) describes the main components of the current account, they

are payments for:

a. Merchandise (goods and services) : merchandise exports and imports

represent tangible assets, while the service exports and imports

represent tourism and other services, such as insurance.

b. Factor income payments : income (interest and dividend payments)

received by investors on foreign investment in financial assets.

c. Transfer payments : represented by aid and grants.

According to Case,et. al, (2009:402), the balance of payments is the

record of a country’s transactions in goods, services, and assets with the

other countries, also the record of a country’s sources (supply) and uses

(demand) of foreign exchange. Meanwile the current account balance is

26

net exports of goods + net exports on services + net invetment income +

net transfer payments.

The relationship between current account balance and Credit Default

Swap spreads had conducted by Sand (2012), the result shows that the

higher current account balance led to the lower CDS spreads.

F. Previous Research

Research regarding the effect of macroeconomic variables toward Credit

Default Swap spread had conducted by some of the researchers. The result can

be elaborated as follows:

1. Alexander and Andreas (2008) had conducted a research titled “Regime

dependent determinants of credit default swap spreads”. In their research

Alexander used daily quotes of iTraxx Europe CDS indices and restrict the

analysis to indices with a maturity of 5 years. The data period starts from June

2004 and ends in June 2007. The applied method for analyze are Linear

regression and Markov switching regression. Using Interest rate, stock return

and implied volatility variables, the result shows that All of the variables that

is interest rate, stock return, and implied volatility are significant toward the

CDS spreads.

2. Greatrex (2008) had conducted a research relates to the credit default swap

market’s deteminants. In her research, Greatrex uses 333 firms as the sample

and employ 6 variables to be investigated. She uses Stock retuns, leverage

ratio, volatility, the rating based index, treasury rate, slope of yield curve and

27

investigates the impact of these variables toward CDS spreads. The data for

conducting this research is monthly, from January 2001 until March 2006.

The result shows that a rating-based CDS index is the best predictor of CDS

spread changes. Leverage and volatility, are also key determinants, as these

two variables can explain almost half of the explained variation in monthly

CDS spread changes.

3. Baum and Wan (2010) were conducted a research regarding the

macroeconomic uncertainty and credit default swap spreads on individual

firms. To measures the macroeconomic variables, Baum and Wan use

variance of the GDP growth rate, the index of industrial production and the

returns on the S&P 500 Composite Index and the other variables such as

market value, leverage ratio, return on equity, and dividend payout ratio. For

conducting this research, they used monthly data from January 2001 to

December 2006, and 5 years CDS spreads from 527 firms. The result shows

that the macroeconomic uncertainty is an important determinant of CDS

spreads.

4. Tang and Yan (2010) had conduct a research regarding Market conditions,

default risk and credit spreads. This research is specified on the impact of the

interaction between market and default risk on corporate credit spreads. They

use large scale firm-level CDS data that allow them to investigate the relative

explanatory power of macroeconomic conditions and firm characteristics and

the effect of their interactions. They limit their research on the U.S. corporate

28

bond issuers denominated in US dollars with the period of outstanding CDS

contracts between June 1997 and November 2006. They employ GDP growth

rate, GDP growth volatility, jump risk and investor sentiment to measure the

market conditions; and coefficient of variation in quarterly operating cash

flow, firm growth, cash flow beta, leverage, implied volatility and jump (as

the slope of option implied volatility curve) to measure the firm

characteristics. The result shows that average credit spreads decrease in GDP

growth rate, but increase in GDP growth volatility and jump risk in the equity

market. At the market level, investor sentiment is the most important

determinant of credit spreads. At the firm level, credit spreads generally rise

with cash flow volatility and beta. Implied volatility is the most significant

determinant of default risk among firm-level characteristics.

5. Liu and Morley (2011) was conducted a research regarding Sovereign Credit

Default Swaps and the Macroeconomic. On their research, Liu and Morley

use domestic interest rate and the exchange rate to represent the

macroeconomic variables. The sample that used by the author is the U.S. and

France. These two countries have been selected due to their importance in the

international financial system and because they represent different approaches

to exchange rates as well. The data used from conducting this research is daily

from March 19th

2008 to September 30th

2010 for the U.S. and from August

16th

2005 to September 30th

2010 for the France. The result shows that the

29

exchange rates has the most significant variable, while the domestic interest

rate has a slightly effect on SCDS spreads.

Table 2.2 will be showing the overview of previous research that had

conducted by some of the researchers.

Table 2.2

Overview Previous Research

No Resercher Analysis

model

Dependent

Variables

Independent

Variables

Result Journal

Number

1 Alexander

and Andreas

(2008)

Linear

regression

and

Markov

switching

regression

CDS

spreads

Interest rate,

stock return,

implied

volatility

All of the

variables that is

interest rate,

stock return,

and implied

volatility are

significant

toward the CDS

spreads.

Journal of

Banking &

Finance 32

(2008)

1008–1021

2

Greatrex

(2008)

Multivariat

e

regression

model

CDS

spreads

Stock retuns,

leverage

ratio,

volatility, the

rating based

index,

treasury rate,

slope of yield

curve.

A rating-based

CDS index that

accounts for

both credit risk

and overall

market

conditions is the

best predictor of

CDS spread

changes.

Leverage and

volatility, are

also key

determinants, as

these two

variables can

Fordham

University

Discussion

Paper No:

2008-05,

March

2008

30

No Researcher Analysis

Model

Dependent

Variable

Independent

Variable

Result

explain almost

half of the

explained

variation in

monthly CDS

spread changes.

Journal

Number

3 Baum and

Wan (2010)

OLS and

fixed-effect

regression

CDS

spread

Macroecono

mic

uncertainty

(computed

by the

conditional

variance of

the GDP

growth rate,

the index of

industrial

production

and the

returns on

the S&P 500

Composite

Index.);

market value;

leverage

ratio; ROE;

Dividend

payout ratio.

Macroeconomic

uncertainty has

significant

explanatory

power over and

above that of

traditional

macroeconomic

factors such as

the risk-free

rate and the

Treasury term

spread.

Applied

Financial

Economics

Taylor &

Francis

Journal vol.

20 (15)

page:

1163-1171

4

Tang and

Yan (2010)

Regression

Corporate

credit

spread

Firm CDS

data; GDP

growth rate;

GDP growth

volatility;

Jump risk;

Investor

sentiment;

CVCF; Firms

At the market

level, investor

sentiment is the

most important

determinant of

credit spreads.

At the firm

level, credit

spreads

Journal of

Banking

and

Finance 34,

743-753,

2010

31

No

Researcher Analysis

Model

Dependent

Variable

Independent

Variable

growth, Cash

flow beta,

Leverage,

Implied

volatility and

Jump

Result

generally rise

with cash flow

volatility and

beta. Implied

volatility is the

most significant

determinant of

default risk

among firm-

level

characteristics.

Journal

Number

5 Liu and

Morley

(2011)

VaR Sovereign

CDS

spread

Domestic

interest rates

and

Exchange

rates

Exchange rate

has the most

important effect

on sovereign

CDS markets.

Bath

Economic

Research

Papers no

03/1, 2011.

Source: Author

G. Theoretical Framework

Theoretical framework is a conceptual model of how one theorizes or

makes a logical sense of the relationship among the several factors that have

been indentified as important to the problem. The theoretical framework

discusses the interrelationship among the variables that are deemed to be

integral of the situation being investigated. (Sekaran, 2003:87). Figure 2.2 will

show the theoretical framework of this research:

32

Figure 2.2

Theoretical Framework

Sovereign Bond Market

Macroeconomic Variables

GDP growth

(X1)

Inflation

(X2)

Unemployment

(X3)

Import/GDP

(X4)

Current account

balance

(X5)

Sovereign Credit Default Swap Spread (Y)

CCL

ADF

Classic Assumption Test

Test

PLS

FEM

REM

Hypothesis Test

F Test

t Test

Conclusion and Suggestion

PP

Chow test

Stationary Test

Test Normality

Heteroscedasticity

Multicollinearity

Panel Data Regression

Test

Hausman test

Autocorrelation

33

The theoretical framework shows that the relationship between the

dependent and independent variables is causative relationship or cause and

effect. According to Malhotra (2004:85) the purposes of causal research are:

1. To understand which variables that are the cause (independent

variables) and which variables that is the effect (dependent variables)

of a phenomenon.

2. To determine the nature of relationship between the causal variables

and the effect to be predicted.

Based on the illustration of theoretical framework that have served in

figure 2.2, it can be read that the independent variables in this research are:

GDP growth rate (X1), inflation (X2), unemployment (X3), import/GDP (X4),

current account balance (X5) and the dependent variable that is Credit Default

Swap spreads (Y).

H. Hypothesis

The hypothesis for this research concerned on whether there is an effect

on dependent variable toward independent variable or there is no effect on

dependent variable toward independent variable. Hypothesis of this research

can be stated as follows:

1. H0 = There are no effect between macroeconomic variables represented by

GDP growth rate, inflation, unemployment, import/GDP, current

34

account balance toward Credit Default Swap spreads in Asia and

Europe.

H1 = There are an effect between macroeconomic variables represented by

GDP growth rate, inflation, unemployment, import/GDP, current

account balance toward Credit Default Swap spreads in Asia and

Europe.

2. H0 = GDP growth rate does not affect the Credit Default Swap spreads in

Asia and Europe.

H1 = GDP growth rate does affect the Credit Default Swap spreads in

Asia and Europe.

3. H0 = Inflation does not affect the Credit Default Swap spreads in Asia

and Europe.

H1 = Inflation does affect the Credit Default Swap spreads in Asia

and Europe.

4. H0 = Unemployment does not affect the Credit Default Swap spreads in

Asia and Europe.

H1 = Unemployment does affect the Credit Default Swap spreads in

Asia and Europe.

5. H0 = Import/GDP does not affect the Credit Default Swap spreads in Asia

and Europe.

H1 = Import/GDP does affect the Credit Default Swap spreads in

Asia and Europe.

35

6. H0 = Current account balance does not affect the Credit Default Swap

spreads in Asia and Europe.

H1 = Current account balance does affect the Credit Default Swap spreads

in Asia and Europe.

36

CHAPTER III

RESEARCH METHODOLOGY

A. Scope of the Research

This research conducted by using eviews application and gretl to test the

heteroscedasticity. Regression with the panel data used as a method of the

research. This research emphasizes on 5 years maturity Credit Default Swap

spreads in Asia and Europe at the time period of 2009-2013. The data that

used by the author to conduct this research are secondary data, obtained from

Bloomberg LP, www.worldbank.org, and www.quandl.com. The reason that

makes the author select the period of 2009 until 2013 is to figure out the effect

of macroeconomic variables toward CDS spreads after the global crisis.

B. Sampling Method

The sampling method used by the researcher in terms of conducting this

research is judgment sampling or purposive sample. In this method, the data

are collected based on individual consideration. If the researcher convinces

that the respondent is appropriate to be the object of the research, so that, the

object can be chosen by the researcher for conducting the research.

Judgment sampling means that the sample is chosen based on the specific

criteria in accordance with the purposes of the research. The criteria required

to avoid the misspecification on determining the research’s sample which can

affect the accuracy of the research (Karyani, 2006).

37

The sample of this research should have to meet the following criteria:

1. The countries located in Asian and European region.

2. Have a complete data that the author required. Such as have a

complete GDP growth rate, inflation, unemployment, import/GDP,

and current account balance data during the period of the research

which is 2009 until 2013.

Based on the criteria described above, the samples that had chosen by the

author for conducting this research are 9 Asian countries and 5 European

countries. The selected Asian countries are China, Hong Kong, Indonesia,

Japan, Malaysia, Philippines, South Korea, Thailand, and Vietnam, while the

European countries are Germany, Ireland, Italy, Portugal, and Spain.

C. Data Collection Method

The data that used for conducting this research is secondary data.

Secondary data gathered through such existing sources or with the other word,

the data is already exist and do not have to be collected by the researcher

(Sekaran, 2003:59). The required data for conducting this research is

macroeconomic variables and sovereign CDS spreads. The data of

macroeconomic variables that consist of GDP growth rate, inflation,

unemployment, import/GDP, current account balance obtained from World

Bank group and World development indicators (www.worldbank.org) and

www.quandl.com, sovereign CDS spreads data obtained from Bloomberg LP

and Indonesian Ministry of Finance (access from Bloomberg LP). The data

38

used for conducting this research is annually data from 2009-2013. The data

for literature study is obtained from internet database, books, journals and

other references.

D. Analysis Technique

1. Analysis Method

The researcher is using the panel data regression analysis to

statistically test the hypothesis. Since, this research is types of causative

research, so, the purpose of this research is to reveal the relationship

between independent variable and dependent variable. Eviews program is

used to know the result.

2. Stationary Test

Panel data that used in this research is the dynamic panel regression.

In dynamic panel regression each variables requires stationary test.

According to Granger and Newbold in Ariefianto (2012:124), the

stationary test aimed to avoid the phenomena that known as spurious

regression. Spurious or nonsense regression is a phenomena where a

regression equation has a good siginificance (high R2) even though there

is no meaningful relationship between the two variables (Gujarati,

2004:792). The hypothesis for stationary test is:

a. H0 : The data contains of unit root ( Non-stationary)

b. H1 : The data does not contaion of unit root ( Stationary)

39

According to Gujarati (2004:815), we can reject the H0 of non-

stationary if the probability is < 0.05.

In this research, the stationary tested using Levin, Lin & Chu t, ADF,

and PP unit root test. If the data is not stationer on the level, we can move

to the first difference or the second difference test.

3. Classic Assumption Test

a. Normality Test

According to Gujarati (2004:147), there are three tests that can be

used to detect the normality problem, they are: (1) histogram of

residuals, (2) normal probability plot (NPP), (3) Jarque–Bera test. In

eviews, the most commonly used test to detect the normality problem

is Jarque–Bera test. The hypothesis for Jarque–Bera test for normality

is:

H0 = residuals are normally distributed

H1 = residuals are not normally distributed

The requirement to reject the H0 is: H0 is rejected if the JB value

is > chi square table, or if the probability of the Jarque–Bera is < α

(0.05). Meanwhile, H0 is accepted if the JB value is < chi square table,

the probability of the Jarque–Bera is > α (0.05).

40

b. Heteroscedasticity Test

If heteroscedasticity is detected in a regression model, thus the

standard error from regression can be refraction (bias). As a

consequences, all of the hypothesis test can be mislead. According to

Ariefinto (2012:39) heteroscedasticity problem causes the conclution

that concluded become invalid.

According to Fadhliyah (2008), there are several ways to detect

the heteroscedasticity, it depends on the software used for conducting

the research. In eviews, white heteroscedasticity is used to test the

heteroscedasticity.

Beside eviews, the other application named gretl can be used to

detect heteroscedasticity through white heteroscedasticity test.

The following hypothesis is built for heteroscedasticity test:

(1) H0 : Heteroscedasticity is not present (Homoscedastic)

(2) H1 : Heteroscedastic

According to Adkins (2011:173), the white test in gretl is similar

with the Breusch-Pagan test. The assumption to reject the null

hypothesis is: if the p-value < α (0.05), thus H0 is rejected. Meanwhile,

if the p-value is > α (0.05), thus H0 is accepted.

41

In gretl, the simplest way to tackle heteroscedasticity problem is

to use least squares to estimate the intercept and slopes and use an

estimator of least squares covariance that is consistent. This is the so-

called heteroscedasticity robust estimator of covariance (Adkins,

2011:176).

Meanwhile, in eviews, Heteroscedasticity can be eliminated

through White’s cross-section standard errors, if heteroscedasticity

caused by the cross section or White’s period standard errors, if

heteroscedasticity caused by the variability over the time. If the

heteroscedasticity caused by both cross section and time series, thus

the it can eliminate through White’s diagonal standard errors.

c. Multicollinearity Test

The independent variables which contain of multicollinearity

make the coefficient of regression become unsuitable with the

substances, thus the interpretation become inappropriate (Fadhliyah,

2008).

According to (Wibowo, 2012: 87), one way to detect

multicollinearity in SPSS is to use a test tools that called Variance

Inflation Factor (VIF).

According to Nachrowi and Usman (2006) in Fadhliyah (2008),

the strong multicollinearity has value > 0.8.

42

d. Autocorrelation Test

According to Ariefianto (2012:30) the commonly uses testing

method to test the autocorrelation is through Durbin-Watson test (DW

tests). The decision making based on the Durbin-Watson test can be

categorized into:

1) 4 – d1 < DW < 4 ; indicates the negative autocorrelation

2) 4 – du < DW < 4 – dl ; indicates the indeterminate

(3) 2 < DW < 4 – du ; indicates that there is no autocorrelation

(4) d1 < dw < du ; indicates the indeterminate

(5) 0 < DW < dL ; indicates the postive autocorrelation

The value of du and dl acquired from Durbin Watson statistic

table.

According to Gujarati (2004:475), if a research is using Generalized

Least Square (GLS) model, then a model contains of autocorrelation

problem in a panel data, thus, the output will be free from

autocorrelation problem.

4. Panel Data Regression

Panel data (also known as longitudinal or cross-sectional time-series

data) is a dataset in which the behavior of entities is observed cross

section (across time). Table 3.1 will show the difference between cross

sectional and time series data

43

Table 3.1

The Difference between Cross section and Time series Data

Year Country X1 X2 Y

2009 A 4.3 9.2 26

2010 A 4.1 10.4 40

2011 A 4.1 9.3 53

2009 B 5.2 4.6 18

2010 B 4.3 6.2 36

2011 B 3.4 6.5 22

2009 C 7.5 2.8 41

2010 C 7.3 1.6 27

2011 C 7 2.8 10

According to Gujarati (2004:640), the advantages of using panel data

are:

a. Panel data give more informative data, more variability, more

degrees of freedom and more efficiency.

b. Panel data are better suited to study the dynamics of change.

c. Panel data can better detect and measure effects that simply cannot

be observed in pure cross-section or pure time series data.

d. By making data available for several thousand units, panel data

can minimize the bias that might result if we aggregate individuals

or firms into broad aggregates.

Time

dimension

Cross

section

44

There are three models on data panel analysis, they are: Pooled

Ordinary Least Square (PLS), Fixed Effect Model (FEM), and Random

Effect Model (REM). The alternative model in panel data is selected after

complete the Chow test, Hausman test.

a. Pooled Ordinary Least Square (PLS)

According to Nachrowi and Usman (2006:311) generally, the

technique used in PLS is similar with the commonly used regression.

The difference between PLS and commonly used regression is, in

panel data, we have to combine the cross section and time series (pool

data) before we define the regression model. The following equation

shows the combination of cross section and time series or pool data:

Where:

N = the number of cross section (individual).

T = the number of time period.

α = alpha

β = beta

With the assumption of error component, we can separately

estimate each unit of individual cross section. The regression

individual cross section equation is:

Yit = α + βXit + εit ;

i = 1, 2, ….., N ; t = 1,2, ……, T

45

For t = 1 the equation is:

Source: Nachrowi and Usman (2006:312)

b. Fixed Effect Model (FEM)

In FEM there is a possibility of change in α on each i and t

(Nachrowi and Usman, 2006:313) FEM is consider on the differences

in parametric value on both cross-section and time series through add

the dummy variable (Fadhliyah, 2008). The equation for FEM is:

(Nachrowi and Usman, 2006:313)

Where:

Yit = dependent variable for individual i and t period

Xit = independent variable for individual i and t period

α = alpha

β = beta

Yit = α + βXit + εit ;

i = 1, 2, ….., N

Yi1 = α + βXi1 + εi1 ;

i = 1, 2, ….., N

Yit = α + βXit + γ2 W2t + …. γN WNt + δ2 Zi2 + …. + δT ZiT + εit

46

γ = N-1

δ = T-1

Wit and Zit are dummy variable that can be defined as:

Wit = 1; for individual i

= 0; for the others

Zit = 1 for period t

= 0; for the others

c. Random Effect Model (REM)

REM model assumes that the sample is randomly acquired on

each period of time. The advantage of using this model compare with

FEM model is, this model is the retrenchment to the number of

variable used. The formula of REM is:

Nachrowi and Usman (2006:316)

Where:

ui = cross-section error component

vt = time-series error component

wit = the combination of cross-section and time-series error

component

Yit = α + βXit + εit ;

εit = ui + vt+ wit

47

1) Model Selection on Panel Data

There are some of the tests that should have to be done

before determine the model that will be used in panel data

method. The first is chow test. Chow test is used to determine

whether Pooled Least Square or Fixed Effect Model that will

be use to processing the data. If the result shows the

significance (H0 is rejected), the test will be continues to

Hausman test. Hausman test is used to determine whether the

Fixed Effect Model or Random Effect Model. If the result of

Hausman test is significant (H0 is rejected), it can concluded

that the data processing used the FEM.

a) Chow Test

Chow test or likelihood test also known as F statistic

test. The aim of chow test is to determine whether PLS or

FEM that will be used to processing the data. The

hypothesis for chow test is:

H0 = PLS model

H1 = FEM model

The equation for chow test is:

URSS F0 = RRSS – URSS / (N-1)

URSS / (NT-N-K)

48

Where:

RRSS = Restricted Residual Sum Square (residual sum

square for PLS model)

URSS = Unrestricted Residual Sum Square (residual sum

square for FEM model)

N = the number of cross-section data

T = the number of time-series data

K = the number of explanatory variable

The requirements to reject H0 is: H0 is rejected if the

F chow is > F table or if the probability of F chow is < α

(0.05).

b) Hausman Test

The aim of Hausman test is to determine whether

FEM or REM that will be used to processing the data. The

hypothesis for Hausman test is:

H0 = REM model

H1 = FEM model

The base for reject the H0 is using statistic

consideration of chi square. If the Hausman test is

49

significant (probability of Hausman < α), thus the null

hypothesis is rejected and the FEM model is used.

5. t-Test

t-test shows how much effect of every single dependent variable

toward the dependent variable. The hypothesis for t-test is:

H0 = the independent variables are not affect the dependent variable

H1 = the independent variable are affect the dependent variable

H0 is rejected if the tstatistic > ttable or if the probability of tstatistic < α. The

siginificance level that used in this research are 1% (0.01), 5% (0.05) and

10% (0.10).

6. F-test

F-test used to measure, do the independent variables simultaneously

affecting the dependent variable. The hypothesis for this test is:

H0 = the independent variables are simultaneously not affecting the

dependent variable

H1 = the independent variables are simultaneously affecting the

dependent variable

H0 is rejected if Fstatistic > Ftable or if the probability of Fstatistic< α

50

7. Adjusted R2

Adjusted R2 is the determination coefficient that explain how much

the dependent variable variant described by the model in the whole. The

value of adjusted R2

is in between 0 and 1. More closer the value of R2 to

the 1, it means that the independent variables perfectly affecting the

dependent variable or with the other word, the model can describe the

variant of dependent variable well.

E. Variable Operational Research

1. Independent Variables

Independent variable is one that influences the dependent variable in

either postivie or negative way. When the independent variable is present,

the dependent variable is also present (Sekaran, 2003:89). The

independent variables used in this research are:

a. GDP Growth

GDP is the sum of gross value added by all resident producers

in the economy plus any product taxes and minus any subsidies not

included in the value of the products. Annual percentage growth

rate of GDP at market prices based on constant local currency.

b. Inflation

Inflation as measured by the Consumer Price Index reflects the

annual percentage change in the cost to the average consumer of

acquiring a basket of goods and services.

51

c. Unemployment

Unemployment rate is the ratio of the number of people

employed to the total number of of people in the labor force.

d. Import/GDP

Imports of goods and services represent the value of all goods

and other market services received from the rest of the world. They

include the value of merchandise, freight, insurance, transport,

travel, royalties, license fees, and other services, such as

communication, construction, financial, information, business,

personal, and government services. They exclude compensation of

employees and investment income (formerly called factor services)

and transfer payments (www.worldbank.org).

e. Current Account Balance

Current account balance is the sum current account balance is

net exports of goods, net exports on services, net invetment income

and net transfer payments.

2. Dependent Variable

According to Sekaran (2003: 88), dependent variable is the main

variable that lends itself for investigation as a variable factor. This

research is using CDS spreads as a dependent variable. CDS spreads

reflect market participants assessment of the risk of a default or credit

event associated with the underlying obligation. The CDS premium or

52

spreads are measured in basis points (bps). The CDS spreads with the 5

years maturity is used in this research.

53

CHAPTER IV

FINDING AND ANALYSIS

This chapter consists of several sections that will describe the analysis and

the result of the hypothesis testing.

A. General Description of Research Object

This research uses 14 countries consist of 9 Asian countries and 5

European countris as a sample and 5 years period as the total of the

population. The sample and the popultion had choosen based on the

judgemental or purposive sampling. According to Malhotra (2004: 322)

judgmental sampling means that the population elements are selected based

on the judgment of the researcher. The researcher, exercising judgment or

expertise, chooses the elements to be included in the sample, because he or

she believes that they are representative of the population or they are

otherwise appropriate.

The object of this research are Credit Default Swap spreads, GDP growth,

Inflation, Unemployment, Import/GDP, and Current Account Balance. The

data are obtained from Bloomberg, Indonesian Ministry of Finance,

www.worldbank.org and www.quandl.com.

54

1. Credit Default Swap

Credit Default Swap (CDS) is an instrument that had emerging at the

end of the 20th

century. The function of Credit Default Swap is as a tool to

measure the sovereign risk. It also apply as an instrument that hedge the

bond investing against the possibility of default. The following chart will

shows the development of CDS market since the BIS first started

reporting CDS notionals in 2004. The data provided is based on the

national amount in US dollar.

Figure 4.1

Annual CDS Notionals Outstanding ($)

Source : BIS Semiannual OTC Derivatives Statistics and ISDA

$ 6.4

$ 13.9

$ 28.7

$ 58.2

$ 49.1

$ 32.7 $ 29.9 $ 28.6

$ 25.1

0

10

20

30

40

50

60

70

2004 2005 2006 2007 2008 2009 2010 2011 2012

CDS

outstanding

year

55

International Swaps and Derivatives Association (ISDA, 2012) holds

that the total notional amount of outstanding CDS contracts rose from $2,2

trillion in December of 2002 to $58.2 trillion in December of 2007. The

market size had more than doubled during each of these years but hit a

setback in 2008 as it suffered from worldwide financial instability.

2. Bloomberg LP

Bloomberg LP is a privately held financial software, data and media

company headquartered in New York City. Bloomberg L.P. provides

financial software tools such as an analytics and equity trading platform,

data services and news to financial companies and organizations through

the Bloomberg terminal.

Bloomberg established in 1981. In 1986, 5,000 terminals had been

installed in subscriber offices. Within a few years, ancillary products

including Bloomberg Tradebook (a trading platform), the Bloomberg

Messaging Service, and the Bloomberg newswire were launched.

Bloomberg launched its news services division in 1990. Bloomberg.com

was first established on September 29, 1993 as a financial portal with

information on markets, currency conversion, news and events, and

Bloomberg Terminal subscriptions.

56

B. Data Description

The data processing in this research is conducted by the statistical

application. The application used for data processing in this research are

eviews 7th

version and gretl.

1. Descriptive Statistic

The descriptive statistic is used to figure out the average value,

minimum value, maximum value, and standard deviation of each

dependent and independent variable. To reveal the descriptive statistics on

the data Credit Default Swap spreds (CDS), GDP Growth (GDP), Inflation

(INF), Unemployment (UNEMP), Import/GDP (IMP), Current Account

Balance (CAB), can be seen in the following table:

Table 4.1

Descriptive Statistics

CDS

GDP

INF

UNEMP

IMP

CAB

Mean 183.7091 2.470000 2.724286 7.221429 57.60000 2.018571

Median 123.1200 2.250000 2.500000 5.250000 40.00000 2.100000

Maximum 1081.870 10.40000 18.70000 26.40000 229.0000 15.70000

Minimum 25.49000 -6.400000 -4.500000 0.700000 12.00000 -11.00000

Std. Dev 177.8623 4.077807 3.027049 5.689499 49.05702 4.793694

Source: processed data

Based on table 4.1, the descriptive statistics can be described as

follows:

57

a. CDS Spreads

The average value of CDS spreads variable is 183.70 with the

standard deviation of 177.86. The lowest value is 25.49 bps, while

the highest value is 1081.87 bps.

b. GDP Growth

The average value of GDP growth variable is 2.47, with the

standard deviation of 4.077. The lowest value is -6.4 percent, while

the highest is 10.4 percent.

c. Inflation

The average value of inflation variable is 2.72 percent, with

the standard deviation of 3.02. The lowest value is -4.5 percent,

while the highest value is 18.7 percent.

d. Unemployment

The average value of unemployment variable is 7.22 percent,

with the standard deviation of 5.68. The lowest value is 0.70

percent, while the highest value is 26.40 percent.

e. Import/GDP

The average value of import/GDP variable is 57.60 percent,

with the standard deviation of 49.05. The lowest value is 12

percent, while the highest value is 229 percent.

58

f. Current Account Balance

The average value of current account balance variable is 2.018

percent, with the standard deviation of 4.79. The lowest value is -

11 percent, while the highest value is 15.7 percent.

Table 4.2

CDS Spreads (in bps)

No Countries

Name

2009 2010 2011 2012 2013

1 China 73.24 67.58 147.17 66.31 80

2 Germany 26.33 59.31 102.17 41.8 25.49

3 Hong Kong 47.76 44.66 89.255 45.61 47.03

4 Indonesia 190.35 129.02 208.0 136 233

5 Ireland 158.43 608.65 724.365 220 120

6 Italy 109.3 239.87 484.35 298 168.325

7 Japan 68.1 72 143.215 81.71 40

8 Malaysia 89.48 72.46 146.29 77.86 109.33

9 Philippines 169.24 126.24 192.08 105.69 114

10 Portugal 91.61 500.97 1081.87 442.94 351.695

11 South Korea 85.41 93.88 160.83 68 65.835

12 Spain 113.58

350 380.35 229.5 153.83

13 Thailand 96.28 98.5 182 94.5 128.66

14 Vietnam 235.07 307.26 409.4 211.305 265.99

Source: data processed

Table 4.2 shows that the lowest CDS spreads produced by Germany in

2013, that is 25.49 bps. Germany has the lowest value of CDS spreads

because, Germany has contribute to give bailout to the Greece and other

Eurozone countries when it surged by the crisis. Thus, it makes the

investors have a good conviction toward its ability to meets its obligation

and pay interest.

59

Meanwhile, the highest CDS spreads value produced by Portugal in

2011 that is 1081.87 bps. Portugal has the highest value of CDS spreads

because, at the time, Portugal was affected by the crisis that faced by

Greece. Since Portugal and Greece are the member of Eurozone, thus,

Portugal has a great potential to affected by the crisis as an impact of

Greece debt crisis. It makes the investors have a less conviction toward the

Portugal’s ability to pay the interest and principals, thus it makes its CDS

spreads increase sharply in 2011.

Table 4.3

GDP Growth (in percent)

No Countries

Name

2009 2010 2011 2012 2013

1 China 9.2 10.4 9.3 7.7 7.7

2 Germany -5.1 4 3.3 0.7 0.4

3 Hong Kong -2.5 6.8 4.8 1.5 2.9

4 Indonesia 4.6 6.2 6.5 6.3 5.8

5 Ireland -6.4 -1.1 2.2 0.2 -0.3

6 Italy -5.5 1.7 0.4 -2.4 -1.9

7 Japan -5.5 4.7 -0.5 1.4 1.5

8 Malaysia -1.5 7.4 5.1 5.6 4.7

9 Philippines 1.1 7.6 3.6 6.8 7.2

10 Portugal -2.9 1.9 -1.3 -3.2 -1.4

11 South Korea 0.7 6.5 3.7 2.3 3.0

12 Spain -3.8

-0.2 0.1 -1.6 -1.2

13 Thailand -2.3 7.8 0.1 7.7 1.8

14 Vietnam 5.4 6.4 6.2 5.2 5.4

Source: data processed

60

Table 4.3 shows that the lowest GDP growth resulted by Ireland in

2009, that is -6.4%. Ireland has the lowest value of GDP growth as an

impact of subprime mortgage crisis that spreads into the other countries

such as Asia and Europe. Ireland GDP growth started to decrease from

2008. The decreasing of Ireland GDP growth in 2009, led by the

subsequent collapse of its domestic property and construction markets.

Property prices rose more rapidly in Ireland than in any other developed

countries. That made the Ireland GDP growth had the lowest value in 2009.

Meanwhile, the highest GDP growth was produced by China in 2010,

that is 10.4%. China has the highest value of GDP growth, because, China

is listed as one of the world's largest exporter. China is listed as the one of

the world's largest exporter, because, China was applied the China reform.

The China reform programs are eliminate the collective farming system,

continued to expand to libration price, fiscal decentralization, diversified of

banking system, develop the market share, accelerate private sector growth,

and open the free trade and investment internationally. Because of the

China’s reform that was applied by the China’s government, China can

easily recovery from the global crisis and listed as one of the world's

largest exporter.

61

Table 4.4

Inflation (in percent)

No Countries

Name

2009 2010 2011 2012 2013

1 China -0.7 3.3 5.4 2.7 2.6

2 Germany 0.3 1.1 2.1 2 1.5

3 Hong Kong 0.6 2.3 5.3 4.1 4.4

4 Indonesia 4.8 5.1 5.4 4.3 6.4

5 Ireland -4.5 0.9 2.6 1.7 0.5

6 Italy 0.8 1.5 2.7 3 1.2

7 Japan -1.3 -0.7 -0.3 0 0.4

8 Malaysia 0.6 1.7 3.2 1.7 2.1

9 Philippines 4.2 3.8 4.6 3.2 3

10 Portugal -0.8 1.4 3.7 2.8 0.3

11 South Korea 2.8 3 4 2.2 1.3

12 Spain -0.3 1.8 3.2 2.4 1.4

13 Thailand -0.8 3.3 3.8 3 2.2

14 Vietnam 7.1 8.9 18.7 9.1 6.6

Source: data processed

Table 4.4 shows that the lowest inflation rate resulted by Ireland in

2009, that is -4.5%. Ireland has the lowest value of inflation rate (measured

by CPI) because, at the time some of the Eurozone members, such as

Ireland, Cyprus, and Greece have experienced falling prices in recent

months. At that time, the demand of industrial goods outside the energy

sector remains weak, this condition makes the price of those goods remains

low.

Meanwhile, the highest inflation rate resulted by Vietnam in 2011, that

is 18.7%. The high value of inflation rate in Vietnam led by the strong

domestic demand and expanded monetary supply. Food and commodities

make up a large share of household spending and increases in gasoline and

power costs are fuel the inflation. Thus, the Vietnam inflation rate was

62

occur as an impact of the strong domestic demand and expanded monetary

supply.

Table 4.5

Unemployment Total

( measured in percent of total labor force in National estimate)

No Countries

Name

2009 2010 2011 2012 2013

1 China 4.3 4.1 4.1 4.1 4.1

2 Germany 7.7 7.1 5.9 5.4 5.3

3 Hong Kong 5.2 4.3 3.4 3.3 3.1

4 Indonesia 7.9 7.1 6.6 6.1 6.3

5 Ireland 12.0 13.9 14.6 14.7 12.0

6 Italy 7.8 8.4 8.4 10.7 12.2

7 Japan 5.0 5.0 4.5 4.3 4.0

8 Malaysia 3.7 3.4 3.1 3.0 3.1

9 Philippines 7.5 7.3 7.0 7.0 7.1

10 Portugal 9.5 10.8 12.7 15.6 16.3

11 South Korea 3.6 3.7 3.4 3.2 3.1

12 Spain 18.0 20.1 21.6 25.0 26.4

13 Thailand 1.5 1.0 0.7 0.7 0.7

14 Vietnam 2.3 2.3 2.0 1.8 4.4

Source: data processed

Table 4.5 shows that the lowest unemployment rate reached by

Thailand in 2011, 2012, and 2013, that is 0.7%. Thailand’s unemployment

rate is low along with the development of the investment flow in Thailand.

Different from the US and the other developing countries, Thailand is rely

more on the labor intensive rather than the technological used. Thus, that

makes the unemployment level on the Thailand is low.

Meanwhile, the highest unemployment rate reached by Spain in 2013,

that is 26.4%. Spain has the highest value of unemployment rate, as an

63

impact of the collapse of the country's property bubble in 2008. Since the

collapse in 2008 of a Spain's labour-intensive property boom, Spain’s

unemployment rate continue to rise relentlessly. The unemployment rate

continues to rise relentlessly until 2013, and it reached the highest value in

2013. Nevertheless, Spain’s government has negotiated to find a way to

reduce the number of unemployment in Spain.

Table 4.6

Import of Goods and Services/GDP (in percent)

No Countries

Name

2009 2010 2011 2012 2013

1 China 22 26 26 25 24

2 Germany 38 42 45 46 45

3 Hong Kong 183 214 222 224 229

4 Indonesia 21 23 25 26 26

5 Ireland 74 81 81 84 86

6 Italy 24 29 30 29 28

7 Japan 12 14 16 17 20

8 Malaysia 71 76 75 75 73

9 Philippines 33 37 36 34 32

10 Portugal 35 39 40 39 40

11 South Korea 43 46 54 54 49

12 Spain 26 30 32 32 32

13 Thailand 58 64 72 74 70

14 Vietnam 73 80 84 77 90

Source: data processed

Table 4.6 shows that the lowest import/GDP value resulted by Japan in

2009, that is 12%. The impact of the economic crisis on Japan has so far

been relatively moderate, the Japanese trade has been badly hit. In

February 2009, indicate a 43% decrease in Japan volumes of imports. The

64

import decline was explained mainly by the declined volume of imported

mineral fuels, which would in part reflect a drop in the price for crude oil.

Meanwhile, the highest import/GDP value resulted by Hong Kong in

2013, that is 229%. Hong Kong has the highest import/GDP value because

Hong Kong has a free market economy, highly dependent on international

trade and finance. Hong Kong has no tariffs on imported goods, and it

levies excise duties on four commodities, whether imported or produced

locally: hard alcohol, tobacco, hydrocarbon oil, and methyl alcohol. Thus,

that what makes the Hong Kong GDP/import is high compared with the

other countries.

Table 4.7

Current Account Balance (in percent of GDP)

No Countries

Name

2009 2010 2011 2012 2013

1 China 4.9 4.0 1.9 2.3 2.1

2 Germany 6.0 6.3 6.2 7.0 7.5

3 Hong Kong 9.9 7.0 5.6 1.7 2.9

4 Indonesia 2.0 0.7 0.2 -2.7 -3.3

5 Ireland -2.2 1.1 1.3 4.4 6.6

6 Italy -1.9 -3.5 -3.1 -0.4 0.8

7 Japan 2.9 3.7 2.0 1.0 0.7

8 Malaysia 15.7 10.9 11.6 6.1 3.8

9 Philippines 5.6 4.5 3.1 2.8 3.5

10 Portugal -11.0 -10.6 -7.1 -2.1 0.5

11 South Korea 3.6 2.7 2.2 3.5 5.8

12 Spain -4.8 -4.5 -3.8 -1.1 0.7

13 Thailand 8.3 3.1 1.2 -0.4 0.7

14 Vietnam -6.2 -3.7 0.2 5.8 6.6

Source: data processed

65

Table 4.7 shows that the lowest current account balance reached by

Portugal in 2009, that is -11%. The imbalances have amplified the impact

of the crisis. The wage and price run-up occurred in Portugal during the

boom, as a consequences its exports are less competitive on world markets

compare with the other Eurozone countries. As Portugal used the same

currencies as other Eurozone countries, it cannot independently cut interest

rates or devalue their currency to stimulate growth. All these factors will

make it harder to determine the policy independently to decrease the deficit

on the current account.

Meanwhile, the highest current account balance achieved by Malaysia

in 2009. Since the Asian financial crisis until 2013, Malaysia’s current

account balance has consistently recorded a surplus. The highest current

account balance recorded in 2009, that is 15.7%. Exports of goods have

outpaced imports, and have more than offset the deficit in the services and

income accounts. The strong performance of Malaysia’s exports was due

to the broad diversification of products and the expansion of trade in new

markets. In particular, commodity exports, played an important role in

contributing to the increase in the current account surplus.

66

2. Data Processing

Before conducting the panel data regression to test the hypothesis,

firstly, the stationary and classic assumption test should have to do as the

requirement for conducting the panel data regression test. The next stage is

determining the model that will be used in panel data regression method.

After all of the requirements have solved, thus the hypothesis testing is can

be implemented.

a. Stationary Test

According to Granger and Newbold in Ariefianto (2012:124), the

stationary test aimed to avoid the phenomena that known as spurious

regression. Spurious or nonsense regression is a phenomena where a

regression equation has a good siginificance (high R2) even though there

is no meaningful relationship between the two variables (Gujarati,

2004:792). According to Gujarati (2004:815), we can reject the H0 of non-

stationary if the probability of α is < 0.05.

In this research, the stationary tested using Levin, Lin & Chu t,

ADF, and PP unit root test. There are three approaches to test the

stationary, they are: level, first difference, and second difference. If the

data is not stationer on the level, we can move to the first difference or

the second difference test. The stationary test at level will be presented

in the following table:

67

Table 4.8

Stationary Test (at level)

No

Variable

Method

Levin, Lin & Chu

t*

ADF – Fisher Chi-

square

PP – Fisher Chi-

square

Statistic

Prob Statistic Prob Statistic Prob

1 CDS -8.77487 0.0000 41.8714 0.0446 49.8817 0.0067

2 GDP -33.6857 0.0000 103.793 0.0000 106.414 0.0000

3 INF -5.66532 0.0000 47.3777 0.0125 61.3029 0.0003

4 UNEMP -7.22625 0.0000 30.5704 0.2447 46.5753 0.0079

5 IMP -16.9846 0.0000 68.8446 0.0000 84.9933 0.0000

6 CAB -10.4601 0.0000 21.3734 0.8094 32.6039 0.2506

Source: data processed

The requirement to reject the null hypothesis (non-stationary) is

statistic p-value should have to < α (0.05). Based on the stationary test

on the level that served on table 4.8, it can be shown that the probability

of ADF – Fisher Chi-square for unemployment (UNEMP) variable is

0.2447, and the probability of ADF – Fisher Chi-square and PP – Fisher

Chi-square for current account balance (CAB) variables are 0.8094 and

0.2506. It means that the unemployment and current account balance

variables are conceive of stationary problems, thus, these two variables

are not stationer at level.

To solve the stationary problem, the stationary test have to

continued and move to the first difference test. The following table is

shows the result of stationary test at first difference:

68

Table 4.9

Stationary Test (at 1st difference)

No

Variable

Method

Levin, Lin & Chu

t*

ADF – Fisher Chi-

square

PP – Fisher Chi-

square

Statistic

prob Statistic Prob Statistic Prob

1 CDS -8.64109 0.0000 65.4128 0.0001 63.8797 0.0001

2 GDP -24.8321 0.0000 101.795 0.0000 131.585 0.0000

3 INF -13.8433 0.0000 33.3787 0.2221 46.0403 0.0173

4 UNEMP -9.77855 0.0000 26.3421 0.4444 29.5021 0.2887

5 IMP -22.5145 0.0000 85.9519 0.0000 108.829 0.0000

6 CAB -8.64352 0.0000 43.1794 0.0334 51.3706 0.0045

Source: data processed

The requirement to reject the null hypothesis (non-stationary) is

statistic p-value should have to < α (0.05). Based on the stationary test

at the 1st difference that served on table 4.9, it shows that the

probability of ADF – Fisher Chi-square for inflation (INF) variable is

0.2221 and the probability of ADF – Fisher Chi-square and PP – Fisher

Chi-square for unemployment (UNEMP) variables are 0.4444 and

0.2887. It means that the inflation and unemployment variables are

conceive of stationary problems, which is in these two variables are not

stationer at the 1st difference.

Since the 1st difference test for unit root test cannot solve the

stationary problem as well, thus, the stationary test have to continued

and move to second difference test. The following table is shows the

result of stationary test at second difference:

69

Table 4.10

Stationary Test (at 2nd

difference)

No

Variable

Method

Levin, Lin & Chu

t*

ADF – Fisher Chi-

square

PP – Fisher Chi-

square

Statistic

Prob Statistic Prob Statistic Prob

1 CDS -7.48963 0.0000 115.056 0.0000 108.643 0.0000

2 GDP -38.6959 0.0000 129.664 0.0000 129.138 0.0000

3 INF -7.24232 0.0000 67.8396 0.0000 69.6795 0.0000

4 UNEMP -14.7336 0.0000 59.5434 0.0002 58.4277 0.0003

5 IMP -16.0537 0.0000 103.095 0.0000 108.306 0.0000

6 CAB -11.2954 0.0000 89.5322 0.0000 88.0058 0.0000

Source: processed data

The requirement to reject the null hypothesis (non-stationary) is

statistic p-value should have to < α (0.05). Based on the stationary test

at the 2nd

difference that served on table 4.10, it shows that all of the

variables meet the criteria for reject the null hypothesis. Since all of the

variable does not have a stationary problem, thus the research can be

continued to assumption classic test.

b. Classic Assumption Test

1) Normality Test

In eviews, the most commonly used test to detect the normality

problem is Jarque–Bera test. The hypothesis for Jarque–Bera test for

normality is:

H0 = residuals are normally distributed

H1 = residuals are not normally distributed

70

The requirement to reject the H0 is, H0 is rejected if the JB value

is < chi square table, or H0 is rejected if the probability of the

Jarque–Bera is > α (0.05). The result of JB test can be seen in the

following figure:

Figure 4.2

The Result of Jarque–Bera Test for Normality

Based on the figure 4.2, the result of JB test shows that the value

of Jarque–Bera is 358.45 that is more than chi square table, that is

89.39, and the probability of JB is 0.00, that is less than the α (0.05).

thus, it can be concluded that the residuals is conceive of the

normality problem.

According to Wooldridge in Ariefianto (2012:148), one of the

advantage of using the panel data is, it becomes robust toward the

violation of the Gauss Markov assumption, which are:

heteroscedasticity and normality. Since, this research is using the

0

5

10

15

20

25

-300 -200 -100 0 100 200 300 400 500 600 700

Series: Standardized Residuals

Sample 2009 2013

Observations 70

Mean 1.14e-14

Median -22.61085

Maximum 694.1472

Minimum -253.3945

Std. Dev. 136.5552

Skewness 2.514476

Kurtosis 12.87978

Jarque-Bera 358.4595

Probability 0.000000

71

panel data analysis, thus it can be concluded that the residuals is

robust from the normality problem.

2) Heteroscedasticity Test

As mentioned in the chapter 3, one of the way to test the

heteroscedasticity problem is using white heteroscedasticity test. In

this research, the researcher is using gretl application to detect the

heteroscedasticity problem. The assumption to reject the null

hypothesis is: H0 is rejected if the p-value < α (0.05). The result of

white heteroscedasticity test can be seen in the following table:

Table 4.11

The Result of White Heteroscedasticity Test

White's test for heteroskedasticity

OLS, using 70 observations

Dependent variable: uhat^2

coefficient std. error t-ratio p-value

--------------------------------------------------------------

const 83225.7 69221.9 1.202 0.2350

gdpgrowth -5809.09 8547.24 -0.6796 0.4999

inflation 15838.8 18355.5 0.8629 0.3924

unemployment -17444.6 10634.2 -1.640 0.1073

import -2008.52 1461.40 -1.374 0.1756

cab 8231.88 6920.08 1.190 0.2399

sq_gdpgrowth 126.429 690.376 0.1831 0.8555

X2_X3 -760.834 1420.90 -0.5355 0.5948

X2_X4 -166.497 991.457 -0.1679 0.8673

X2_X5 -16.3650 59.5365 -0.2749 0.7846

X2_X6 1399.39 753.605 1.857 0.0693 *

sq_inflation -576.417 610.114 -0.9448 0.3494

X3_X4 396.706 1629.28 0.2435 0.8086

X3_X5 41.9809 130.135 0.3226 0.7484

X3_X6 -1954.38 1021.74 -1.913 0.0616 *

sq_unemployme 130.098 280.936 0.4631 0.6454

X4_X5 330.567 120.936 2.733 0.0087 ***

X4_X6 -1160.93 626.104 -1.854 0.0697 *

sq_import 3.14821 4.68264 0.6723 0.5045

X5_X6 -49.6438 65.8908 -0.7534 0.4548

sq_cab 141.950 276.939 0.5126 0.6106

Unadjusted R-squared = 0.435551

Test statistic: TR^2 = 30.488563,

with p-value = P(Chi-square(20) > 30.488563) = 0.062315

Source: processed data

72

Based on white heteroscedasticity test that provided in the table

4.11, it is known that the p-value 0.062315 is higher than α 0.05, it

means that the null hypothesis of homoscedastic can be accepted.

Therefore, it can be concluded that the data in this research does not

conceive heteroscedasticity.

3) Multicollinearity Test

Multicollinearity test aimed to figure out whether there is a

collinear relationship cross the independent variables.

Multicollinearity problem led by the usage of two variables that have a

derived relationship in one model. According to Nachrowi and Usman

(2006), the strong multicollinearity has value > 0.8. The result of

multicollinearity test can be seen in the following table:

Table 4.12

The Result of Multicollinearity Test

GDP

INF

UNEMP

IMP

CAB

GDP 1.000000 0.494156 -0.486683 0.056883 0.182503

INF 0.494156 1.000000 -0.254561 0.181292 -0.080442

UNEMP -0.486683 -0.254561 1.000000 -0.242006 -0.384453

IMP 0.056883 0.181292 -0.242006 1.000000 0.283485

CAB 0.182503 -0.080442 -0.384453 0.283485 1.000000

Source: processed data

73

Based on the multicollinearity test that provided in the table

4.12, it is known that the value of each variable (GDP, INF,

UNEMP, IMP, CAB) toward the other variables is less than 0.8.

Thus, it can concluded that there is no multicollinearity contained on

GDP growth, inflation, unemployment, import, and current account

balance variable.

4) Autocorrelation Test

The aim of autocorrelation test is to detect the internal

correlation among the groups of a series observation arrange in a

series of place and time. According to Ariefianto (2012:30) the

commonly uses testing method to test the autocorrelation Durbin-

Watson test (DW tests). If the result shows that 2 < DW < 4 – du it

can be concluded that there is no autocorrelation. The result of

Durbin-Watson test can be seen in the following table:

Table 4.13

The Result of Autocorrelation Test

using Durbin-Watson Statistic

DW count

du table

value

4 – du

dL table

value

4 – dL

1.747141 1.8025 2.1975 1.4326 2.5674

Source: data processed

74

Table 4.13 shows the result of autocorrelation test using Durbin-

Watson statistic. As the result in the table 4.13 it is known that

autocorrelation problem is detected. The calculation shows that 2 <

1.747141 < 2.1975. Based on the result, thus it can be concluded that

the data is conceive of autocorrelation problem. Nevertheless,

according to Gujarati (2004:475), if one research is using GLS

(Generalized Least Square) model, thus, the output does not have an

autocorrelation problem. The regression model that have used in this

research is using GLS model, thus it can be concluded that the

autocorrelation problem is solved.

c. Model Selection in Panel Data Regression

As mentioned in the chapter 3, there are three models in panel data

regression, they are: Pooled Least Square (PLS), Fixed Effect Model

(FEM), and Random Effect Model (REM). Before determine which one

is the best model for the research, there are some of test that have to be

done. The first is chow test. Chow test used to determine whether PLS

or FEM model that will be used to processing the data. If the result of

chow test indicates that the FEM is better than PLS, the next stage will

be the hausman test. Hausman test used to determine whether FEM or

REM that will be used to processing the data.

75

1) Chow Test

The aim of chow test is to determine whether PLS or FEM

model that will be used to processing the data. The requirements to

reject H0 is, if the F chow is > F table or if the probability of F chow

is < α (0.05).

Table 4.14

The Result of Chow Test

Cross-section F

F

Chow

F

Table

Probability

F

Probability α

Decision

2.688961 2.36 0.0059 0.05 FEM

Source: processed data

Based on the chow test result that have processed in the table

4.14, it is known that the value of Cross-section F is higher than F

table that is (2.68 > 2.36) and the probability of F chow in Cross-

section F is lower than α tht is (0.0059 < 0.05), thus the H0 (PLS

model) is rejected. Thus, the tentative conclusion is FEM model is

used. After finished the chow test, the next stage will be the

hausman test, to determine whether the FEM or REM model that

will be used to process the data.

76

2) Hausman Test

The aim of hausman test is to determine whether FEM or REM

that will be used to processing the data. The base for reject the H0

(REM) is using statistic consideration of chi square. If the hausman

test is significant (probability of hausman < α), thus the null

hypothesis is rejected and the FEM model is used. The result of

Hausman test will be provided in the following table:

Table 4.15

The Result of Hausman Test

Cross-section

RE

Probability

α Decision

prob

Prob

0.3366 0.05 REM

Source: processed data

Based on the chow test result that have processed in the table

4.15, it is known that the probability of Random Effect is higher

than the probability of α that is 0.3366 > 0.05. Thus, it can be concluded

that the null hypothesis (REM) is accepted and the REM model is

used for processing the data.

77

d. Hypothesis Testing

After testing the stationary test, classic assumption test, and model

selection test, it can be concluded that all of the data that have used for

conducting this research are passed from stationary test, free from

normality, heteroscedasticity, multicollinearity, and autocorrelation

problem. After completing all of the requirements needed, next step

will be test the hypothesis. After selecting the model that will be used,

thus the Random Effect Model in panel data regression is used to

testing the hypothesis.

1) The Impact of Macroeconomic Variables toward Credit Default

Swap Spreads Partially (T-Test)

T-test aimed to observe how much every single dependent

variable is affecting the dependent variable. The hypothesis of t-test

is:

H0 = the independent variables are not affecting the dependent

variable

H1 = the independent variable are affecting the dependent variable

H0 is rejected if the tstatistic > ttable or if the probability of tstatistic < α.

The significance level (α) that used in this research are 1% (0.01),

78

5% (0.05) and 10% (0.10) The result of t-test can be seen in the

following table:

Table 4.16

The Result of t-Test

Variable Coefficient Std. Error t-Statistic Prob.

C 35.74421 61.98233 0.576684 0.5662

GDPGROWTH? -2.531825 5.504267 -0.459975 0.6471

INFLATION? 26.14017 7.410467 3.527466 0.0008***

UNEMPLOYMENT? 14.37829 4.754548 3.024112 0.0036***

IMPORT? -0.068843 0.551008 -0.124939 0.9010

CAB? -8.353070 4.278162 -1.952491 0.0553*

Note: *Significance level at 10%

** Significance level at 5%

*** Significance level at 1%

a. Dependent variable: Credit Default Swap spreads

Source: processed data

The result of the t-test can be interpreted as follows:

a) GDP Growth

GDP growth variable has the probability of 0.5662. The

result shows that the GDP growth variable is not significantly

affect the Credit Default Swap spreads at significance level of

1%, 5% or 10%. The coefficient of GDP growth variable is

negative, it means that the higher GDP growth leads to the

lower Credit Default Swap spread.

79

Variable GDP growth is not significantly affect the CDS

spreads in Asia and Europe because of impact of the crisis. As

mentioned in the table 4.3 from 2009 until 2010 there were a

quite high differences among many countries in term of GDP

growth such as Hong Kong, Malaysia, Italy and Portugal. This

condition occur because in 2009 those countries in 2009 were

facing the global financial crisis while, in 2010 they were

facing recovery phase.

The result of this research is consistent with the research

that had conducted by Tang and Yan (2010) and Brandorf and

Holmberg (2010) where the GDP growth is a variable that is

not affecting the Credit Default Swap spreads.

b) Inflation

Inflation variable has the probability of 0.0008. The result

shows that the inflation variable is significantly affect the

Credit Default Swap spreads at significance level of 1% that is

(0.0008 < 0.01). The coefficient of inflation variable is

positive, it means that the higher inflation leads to the higher

Credit Default Swap spreads.

The result of this research is consistent with the research

that had conducted by Sand (2012) where the inflation is a

variable that is affecting the Credit Default Swap spreads.

80

c) Unemployment

Unemployment variable has the probability of 0.0036. The

result shows that the unemployment variable is significantly

affect the Credit Default Swap spreads at significance level of

1% that is (0.0036 < 0.01). The coefficient of unemployment

variable is positive, it means that the higher unemployment

leads to the higher Credit Default Swap spreads.

The result of this research is consistent with the research

that had conducted by Brandorf and Holmberg (2010) where

the unemployment is a variable that is affecting the Credit

Default Swap spreads.

d) Import/GDP

Import/GDP variable has the probability of 0.9010. The

result shows that the import/GDP variable is not significantly

affect the Credit Default Swap spreads at significance level of

1%, 5% or 10%. The coefficient of import/GDP variable is

negative, it means that the higher import/GDP leads to the

lower Credit Default Swap spreads.

The import/GDP variable is not significantly affect the

Credit Default Swap spreads because the difference on

import/GDP in one country from a year to year tend to be

stable. Thus, there are no significant differences among five

81

years. This condition may lead this variable to be not

significant affect the Credit Default Swap spreads.

The result of this research is inconsistent with the research

that had conducted by Sand (2012) where the import/GDP is a

variable that is affecting the Credit Default Swap spreads.

e) Current Account Balance

Current account balance variable has the probability of

0.0553. The result shows that the current account balance

variable is significantly affect the Credit Default Swap spreads

at significance level of 10% that is (0.0553 < 0.10). The

coefficient of current account balance variable is negative, it

means that the higher current account balance leads to the

lower Credit Default Swap spreads.

The result of this research is consistent with the researcqqh

that had conducted Ho (2014) where in long-run the current

account balance is a variable that is affecting the Credit Default

Swap spreads.

Based on the results that have obtained, thus, it can be

concluded that inflation, unemployment, and current account

balance variables are partially significant affecting Credit

82

Default Swap spreads. Meanwhile, GDP growth and

import/GDP, variables are not partially significant affecting

Credit Default Swap spreads.

2) The Impact of Macroeconomic Variables toward Credit Default

Swap Spreads Simultaneously (F-Test)

F-test used to measure, do the independent variables

simultaneously affecting the dependent variable. The hypothesis of

F-test is:

H0 = the independent variables are simultaneously not affecting the

dependent variable

H1 = the independent variables are simultaneously affecting the

dependent variable

H0 is rejected if Fstatistic > Ftable or if the probability of Fstatistic < α.

The result of F-test can be seen in the following table:

83

Table 4.17

The Result of F-Test

a. Predictors: constant, GDP, INF,UNEMP, IMP, CAB

b. Dependent variable: Credit Default Swap spreads

Source: processed data

Based on the F-test result that have provided in the table

4.17, it is known that the probability of Fstatistic is 0.000139 which is

less than α that is (0.000139 < 0.05). Thus, it can be concluded that

the independent variables that consist of GDP growth, inflation,

unemployment, import/GDP, and current account balance are

simultaneously affect the dependent variable that is Credit Default

Swap spreads.

e. Regression Equation

Based on the panel data regression test using Random Effect

Model, that shown on the table 4.14, the regression model will be:

Y = 35.74421 + 26.14017X2 + 14.37829X3 - 8.353070X5

Weighted Statistics

R-squared 0.317519 Mean dependent var 104.7162

Adjusted R-squared 0.264200 S.D. dependent var 143.4075

S.E. of regression 123.0132 Sum squared resid 968463.2

F-statistic 5.955104 Durbin-Watson stat 1.747141

Prob(F-statistic) 0.000139

84

Where:

Y = Credit Default Swap spreads

X2 = Inflation

X3 = Unemployment

X5 = Current Account Balance

The regression equality shows that the regression coefficient of

constanta is positive. That means that by assuming that the value of

GDP growth, inflation, unemployment, import/GDP, and current

account balance variables are constant (0), thus the value of Credit

Default Swap spreads is 35.744.

The regression equality shows that the regression coefficient of

inflation variable is positive. That means that by assuming the other

independent variables are constant, the increasing of inflation variable

of 1% will led the Credit Default Swap spreads have a tendency to be

rise 26.140.

The regression equality shows that the regression coefficient of

unemployment variable is positive. That means that by assuming the

other independent variables are constant, the increasing of

unemployment variable of 1% will led the Credit Default Swap spreads

have a tendency to be rise 14.378.

The regression equality shows that the regression coefficient of

current account balance variable is negative. That means that by

85

assuming the other independent variables are constant, the increasing of

current account balance variable of 1% will led the Credit Default

Swap spreads have a tendency to be down 8.353

f. The Effect of Macroeconomic Variables in Explaining the Credit

Default Swap spreads

Adjusted R2 is the determination coefficient that explain how much

the dependent variable variant described by the model in the whole.

The value of adjusted R2 is in between 0 and 1. More closer the value of

R2 to the 1, it means the independent variables perfectly affect the

dependent variable or with the other word, the model can describe the

variant of dependent variable well.

Based on the calculation that have provided on the table 4.17, it is

known that the value of adjusted R2 is 0.264200 or 26.42%. That

means that 26.42% dependent variable that is Credit Default Swap

spreads can be explained by the independent variables that are GDP

Growth, inflation, unemployment, import/GDP, and current account

balance. While the rest, 73.58% of dependent variable is explained by

the other variables which are not included in this research.

86

CHAPTER V

CONCLUSION

A. Conclusion

The purpose of this research is to find out the impact of macroeconomic

variables that represented by GDP growth, inflation, unemployment,

import/GDP, and current account balance toward Credit Default Swap

spread`s in Asia and Europe, using the sample of 9 selected Asian countries

and 5 selected European countries. The data that used for conducting this

research are from the period of 2009 until 2013. Based on the research that

has been conducted, the results are as follows:

1. Based on the panel data regression that has been conducted, the result

shows that the inflation, unemployment, and current account balance

variables are affecting the Credit Default Swap spreads. Meanwhile, the

GDP growth, and import/GDP variables are not affecting the Credit

Default Swap spreads. Inflation variable has positively affect the Credit

Default Swap spreads. The result is consistent with the research that had

conducted by Sand (2012), where the inflation variable has a positive sign

toward CDS spreads. Unemployment variable has positively affect the

Credit Default Swap spreads. The result is consistent with the research

that had conducted by Brandorf and Holmberg (2010), where the

unemployment variable has a positive sign toward CDS spreads. Current

account balance variable has negatively affect the Credit Default Swap

87

spreads. The result is consistent with the research that had conducted by

Ho (2014), where the current account balance variable has a negative sign

toward CDS spreads.

2. Based on the panel data regression that has been conducted, the result of

adjusted R2 test analysis is 0.264200. It means that the independent

variables that is represented by GDP growth, inflation, unemployment,

import/GDP, and current account balance can explain the dependent

variable which is Credit Default Swap spreads with the accuracy of

26.42%. While the rest of 73.58% is explained by the other variables that

are not mentioned on this research.

B. Suggestion

Based on the conclusion that had elaborated above, the suggestion can be

stated as follows:

1. For Investors

For the investors, especially the bond investor’s who want to buy the

Credit Default Swap as a hedging instrument, need to pay attention on the

macroeconomic variables that might affect the movement of Credit

Default Swap spreads, especially inflation, unemployment and current

account balance which are significantly affecting the Credit Default Swap

spreads.

88

2. For academics

The result of this research “The Impact of Macroeconomic Variables

toward Credit Default Swap Spreads” can be used to give the additional

insight to the academics. Thus, the academics can conduct the more

appropriate research relates to the impact of macroeconomic variables

toward Credit Default Swap spreads.

3. For Government

This research is expected to gives a consideration for the government

before conducting the policies especially related to the macroeconomic

variables, and consider the impact of the macroeconomic policies that

might impact the market, especially the financial market.

C. The Limits of the Research and Recommendation

This research have some of limitations that might be restrict the scope

of the research. The boundaries are:

1. The observation period is limited to the period of 2009-2013.

2. This research is not consider on the other factors that expected have an

impact toward the Credit Default Swap Spreads.

3. This research is limited the Asian countries only for 9 countries and

European countries only for 5 countries.

The recommendation that might be used for enhance the research, are:

89

1. For further Researchers

a. The observation period should have to be expended in order to

more accurately observe the impact macroeconomic variables

toward Credit Default Swap spreads.

b. The observation period should have to enlarged not only annually,

it can be quarterly, or monthly.

c. The other factors that might affecting the Credit Default Swap

spreads should have to included in the next research in order to

make the result of the research to be more accurate.

d. The population of the research further should have to be add, not

only Asia and Europe, but also the other region such as America,

Africa, etc.

2. For the investors

Investors can use the GDP growth, inflation, unemployment,

import/GDP, and current account balance information as one of the

resources for make a consideration on Credit Default Swap spreads.

However, remember that there are a lot of factors except the five

variables above that might affecting the Credit Default Swap spreads,

the researcher is recommend to the investors to consider on the other

factors that might impact the Credit Default Swap spreads before

taking the hedging decision.

90

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94

APPENDIX I

CDS Spreads (in bps)

(2009-2013)

No Countries

Name

2009 2010 2011 2012 2013

1 China 73.24 67.58 147.17 66.31 80

2 Germany 26.33 59.31 102.17 41.8 25.49

3 Hong Kong 47.76 44.66 89.255 45.61 47.03

4 Indonesia 190.35 129.02 208.0 136 233

5 Ireland 158.43 608.65 724.365 220 120

6 Italy 109.3 239.87 484.35 298 168.325

7 Japan 68.1 72 143.215 81.71 40

8 Malaysia 89.48 72.46 146.29 77.86 109.33

9 Philippines 169.24 126.24 192.08 105.69 114

10 Portugal 91.61 500.97 1081.87 442.94 351.695

11 South Korea 85.41 93.88 160.83 68 65.835

12 Spain 113.58

350 380.35 229.5 153.83

13 Thailand 96.28 98.5 182 94.5 128.66

14 Vietnam 235.07 307.26 409.4 211.305 265.99

GDP Growth (in percent)

(2009-2013)

No Countries

Name

2009 2010 2011 2012 2013

1 China 9.2 10.4 9.3 7.7 7.7

2 Germany -5.1 4 3.3 0.7 0.4

3 Hong Kong -2.5 6.8 4.8 1.5 2.9

4 Indonesia 4.6 6.2 6.5 6.3 5.8

5 Ireland -6.4 -1.1 2.2 0.2 -0.3

6 Italy -5.5 1.7 0.4 -2.4 -1.9

7 Japan -5.5 4.7 -0.5 1.4 1.5

8 Malaysia -1.5 7.4 5.1 5.6 4.7

9 Philippines 1.1 7.6 3.6 6.8 7.2

10 Portugal -2.9 1.9 -1.3 -3.2 -1.4

11 South Korea 0.7 6.5 3.7 2.3 3.0

12 Spain -3.8

-0.2 0.1 -1.6 -1.2

13 Thailand -2.3 7.8 0.1 7.7 1.8

14 Vietnam 5.4 6.4 6.2 5.2 5.4

95

Inflation (in percent)

(2009-2013)

No Countries

Name

2009 2010 2011 2012 2013

1 China -0.7 3.3 5.4 2.7 2.6

2 Germany 0.3 1.1 2.1 2 1.5

3 Hong Kong 0.6 2.3 5.3 4.1 4.4

4 Indonesia 4.8 5.1 5.4 4.3 6.4

5 Ireland -4.5 0.9 2.6 1.7 0.5

6 Italy 0.8 1.5 2.7 3 1.2

7 Japan -1.3 -0.7 -0.3 0 0.4

8 Malaysia 0.6 1.7 3.2 1.7 2.1

9 Philippines 4.2 3.8 4.6 3.2 3

10 Portugal -0.8 1.4 3.7 2.8 0.3

11 South Korea 2.8 3 4 2.2 1.3

12 Spain -0.3 1.8 3.2 2.4 1.4

13 Thailand -0.8 3.3 3.8 3 2.2

14 Vietnam 7.1 8.9 18.7 9.1 6.6

Unemployment Total

( measured in percent of total labor force in National estimate)

(2009-2013)

No Countries

Name

2009 2010 2011 2012 2013

1 China 4.3 4.1 4.1 4.1 4.1

2 Germany 7.7 7.1 5.9 5.4 5.3

3 Hong Kong 5.2 4.3 3.4 3.3 3.1

4 Indonesia 7.9 7.1 6.6 6.1 6.3

5 Ireland 12.0 13.9 14.6 14.7 12.0

6 Italy 7.8 8.4 8.4 10.7 12.2

7 Japan 5.0 5.0 4.5 4.3 4.0

8 Malaysia 3.7 3.4 3.1 3.0 3.1

9 Philippines 7.5 7.3 7.0 7.0 7.1

10 Portugal 9.5 10.8 12.7 15.6 16.3

11 South Korea 3.6 3.7 3.4 3.2 3.1

12 Spain 18.0 20.1 21.6 25.0 26.4

13 Thailand 1.5 1.0 0.7 0.7 0.7

14 Vietnam 2.3 2.3 2.0 1.8 4.4

96

Import of Goods and Services/GDP (in percent)

(2009-2013)

No Countries

Name

2009 2010 2011 2012 2013

1 China 22 26 26 25 24

2 Germany 38 42 45 46 45

3 Hong Kong 183 214 222 224 229

4 Indonesia 21 23 25 26 26

5 Ireland 74 81 81 84 86

6 Italy 24 29 30 29 28

7 Japan 12 14 16 17 20

8 Malaysia 71 76 75 75 73

9 Philippines 33 37 36 34 32

10 Portugal 35 39 40 39 40

11 South Korea 43 46 54 54 49

12 Spain 26 30 32 32 32

13 Thailand 58 64 72 74 70

14 Vietnam 73 80 84 77 90

Current Account Balance (in percent of GDP)

(2009-2013)

No Countries

Name

2009 2010 2011 2012 2013

1 China 4.9 4.0 1.9 2.3 2.1

2 Germany 6.0 6.3 6.2 7.0 7.5

3 Hong Kong 9.9 7.0 5.6 1.7 2.9

4 Indonesia 2.0 0.7 0.2 -2.7 -3.3

5 Ireland -2.2 1.1 1.3 4.4 6.6

6 Italy -1.9 -3.5 -3.1 -0.4 0.8

7 Japan 2.9 3.7 2.0 1.0 0.7

8 Malaysia 15.7 10.9 11.6 6.1 3.8

9 Philippines 5.6 4.5 3.1 2.8 3.5

10 Portugal -11.0 -10.6 -7.1 -2.1 0.5

11 South Korea 3.6 2.7 2.2 3.5 5.8

12 Spain -4.8 -4.5 -3.8 -1.1 0.7

13 Thailand 8.3 3.1 1.2 -0.4 0.7

14 Vietnam -6.2 -3.7 0.2 5.8 6.6

97

APPENDIX II

Table 4.1

Descriptive Statistics

CDS?

GDP?

INF?

UNEMP?

IMP?

CAB?

Mean 183.7091 2.470000 2.724286 7.221429 57.60000 2.018571

Median 123.1200 2.250000 2.500000 5.250000 40.00000 2.100000

Maximum 1081.870 10.40000 18.70000 26.40000 229.0000 15.70000

Minimum 25.49000 -6.400000 -4.500000 0.700000 12.00000 -11.00000

Std. Dev. 177.8623 4.077807 3.027049 5.689499 49.05702 4.793694

Skewness 2.643070 -0.182738 2.144316 1.521385 2.345209 -0.175444

Kurtosis 11.81431 2.143483 12.83088 5.001259 8.244334 3.785684

Jarque-Bera 308.1029 2.529314 335.5289 38.68518 144.3839 2.159563

Probability 0.000000 0.282336 0.000000 0.000000 0.000000 0.339670

Sum 12859.64 172.9000 190.7000 505.5000 4032.000 141.3000

Sum Sq.

Dev. 2182814. 1147.367 632.2487 2233.558 166054.8 1585.586

Observations 70 70 70 70 70 70

Cross

sections 14 14 14 14 14 14

98

APPENDIX III

Stationary Test

Stationary Test (at level)

Pool unit root test: Summary

Series: CDSSPREADS_CHINA, CDSSPREADS_HONGKONG,

CDSSPREADS_INDONESIA, CDSSPREADS_JAPAN,

CDSSPREADS_MALAYSIA, CDSSPREADS_PHILIPPINES,

CDSSPREADS_SOUTHKOREA, CDSSPREADS_THAILAND,

CDSSPREADS_VIETNAM, CDSSPREADS_GERMANY,

CDSSPREADS_IRELAND, CDSSPREADS_ITALY,

CDSSPREADS_PORTUGAL, CDSSPREADS_SPAIN

Date: 10/17/14 Time: 13:21

Sample: 2009 2013

Exogenous variables: Individual effects

Automatic selection of maximum lags

Automatic lag length selection based on SIC: 0

Newey-West automatic bandwidth selection and Bartlett kernel

Balanced observations for each test Cross-

Method Statistic Prob.** sections Obs

Null: Unit root (assumes common unit root process)

Levin, Lin & Chu t* -8.77487 0.0000 14 56

Null: Unit root (assumes individual unit root process)

ADF - Fisher Chi-square 41.8714 0.0446 14 56

PP - Fisher Chi-square 49.8817 0.0067 14 56 ** Probabilities for Fisher tests are computed using an asymptotic Chi

-square distribution. All other tests assume asymptotic normality.

99

Pool unit root test: Summary

Series: GDPGROWTH_CHINA, GDPGROWTH_HONGKONG,

GDPGROWTH_INDONESIA, GDPGROWTH_JAPAN,

GDPGROWTH_MALAYSIA, GDPGROWTH_PHILIPPINES,

GDPGROWTH_SOUTHKOREA, GDPGROWTH_THAILAND,

GDPGROWTH_VIETNAM, GDPGROWTH_GERMANY,

GDPGROWTH_IRELAND, GDPGROWTH_ITALY,

GDPGROWTH_PORTUGAL, GDPGROWTH_SPAIN

Date: 10/17/14 Time: 15:30

Sample: 2009 2013

Exogenous variables: Individual effects

Automatic selection of maximum lags

Automatic lag length selection based on SIC: 0

Newey-West automatic bandwidth selection and Bartlett kernel

Balanced observations for each test Cross-

Method Statistic Prob.** sections Obs

Null: Unit root (assumes common unit root process)

Levin, Lin & Chu t* -33.6857 0.0000 14 56

Null: Unit root (assumes individual unit root process)

Im, Pesaran and Shin W-stat -12.5420 0.0000 14 56

ADF - Fisher Chi-square 103.793 0.0000 14 56

PP - Fisher Chi-square 106.414 0.0000 14 56 ** Probabilities for Fisher tests are computed using an asymptotic Chi

-square distribution. All other tests assume asymptotic normality.

100

Pool unit root test: Summary

Series: INFLATION_CHINA, INFLATION_HONGKONG, INFLATION_INDON

ESIA, INFLATION_JAPAN, INFLATION_MALAYSIA, INFLATION_PHILIPP

INES, INFLATION_SOUTHKOREA, INFLATION_THAILAND,

INFLATION_VIETNAM, INFLATION_GERMANY, INFLATION_IRELAND,

INFLATION_ITALY, INFLATION_PORTUGAL, INFLATION_SPAIN

Date: 10/17/14 Time: 13:26

Sample: 2009 2013

Exogenous variables: Individual effects

Automatic selection of maximum lags

Automatic lag length selection based on SIC: 0

Newey-West automatic bandwidth selection and Bartlett kernel

Balanced observations for each test Cross-

Method Statistic Prob.** sections Obs

Null: Unit root (assumes common unit root process)

Levin, Lin & Chu t* -5.66532 0.0000 14 56

Null: Unit root (assumes individual unit root process)

ADF - Fisher Chi-square 47.3777 0.0125 14 56

PP - Fisher Chi-square 61.3029 0.0003 14 56 ** Probabilities for Fisher tests are computed using an asymptotic Chi

-square distribution. All other tests assume asymptotic normality.

Pool unit root test: Summary Series: UNEMPLOYMENT_CHINA, UNEMPLOYMENT_HONGKONG,

UNEMPLOYMENT_INDONESIA, UNEMPLOYMENT_JAPAN, UNEMPLOYMENT_MALAYSIA, UNEMPLOYMENT_PHILIPPINES, UNEMPLOYMENT_SOUTHKOREA, UNEMPLOYMENT_THAILAND, UNEMPLOYMENT_VIETNAM, UNEMPLOYMENT_GERMANY, UNEMPLOYMENT_IRELAND, UNEMPLOYMENT_ITALY, UNEMPLOYMENT_PORTUGAL, UNEMPLOYMENT_SPAIN

Date: 10/17/14 Time: 13:29 Sample: 2009 2013 Exogenous variables: Individual effects Automatic selection of maximum lags Automatic lag length selection based on SIC: 0 Newey-West automatic bandwidth selection and Bartlett kernel Balanced observations for each test

Cross-

Method Statistic Prob.** sections Obs

Null: Unit root (assumes common unit root process)

Levin, Lin & Chu t* -7.22625 0.0000 13 52

Null: Unit root (assumes individual unit root process)

ADF - Fisher Chi-square 30.5704 0.2447 13 52 PP - Fisher Chi-square 46.5753 0.0079 13 52

** Probabilities for Fisher tests are computed using an asymptotic Chi -square distribution. All other tests assume asymptotic normality.

101

Pool unit root test: Summary

Series: IMPORT_CHINA, IMPORT_HONGKONG, IMPORT_INDONESIA,

IMPORT_JAPAN, IMPORT_MALAYSIA, IMPORT_PHILIPPINES,

IMPORT_SOUTHKOREA, IMPORT_THAILAND, IMPORT_VIETNAM,

IMPORT_GERMANY, IMPORT_IRELAND, IMPORT_ITALY,

IMPORT_PORTUGAL, IMPORT_SPAIN

Date: 10/17/14 Time: 13:37

Sample: 2009 2013

Exogenous variables: Individual effects

Automatic selection of maximum lags

Automatic lag length selection based on SIC: 0

Newey-West automatic bandwidth selection and Bartlett kernel

Balanced observations for each test Cross-

Method Statistic Prob.** sections Obs

Null: Unit root (assumes common unit root process)

Levin, Lin & Chu t* -16.9846 0.0000 13 52

Null: Unit root (assumes individual unit root process)

ADF - Fisher Chi-square 68.8446 0.0000 13 52

PP - Fisher Chi-square 84.9933 0.0000 13 52 ** Probabilities for Fisher tests are computed using an asymptotic Chi -square distribution. All other tests assume asymptotic normality.

Pool unit root test: Summary

Series: CAB_CHINA, CAB_HONGKONG, CAB_INDONESIA, CAB_JAPAN,

CAB_MALAYSIA, CAB_PHILIPPINES, CAB_SOUTHKOREA,

CAB_THAILAND, CAB_VIETNAM, CAB_GERMANY, CAB_IRELAND,

CAB_ITALY, CAB_PORTUGAL, CAB_SPAIN

Date: 10/17/14 Time: 13:38

Sample: 2009 2013

Exogenous variables: Individual effects

Automatic selection of maximum lags

Automatic lag length selection based on SIC: 0

Newey-West automatic bandwidth selection and Bartlett kernel

Balanced observations for each test Cross-

Method Statistic Prob.** sections Obs

Null: Unit root (assumes common unit root process)

Levin, Lin & Chu t* -10.4601 0.0000 14 56

Null: Unit root (assumes individual unit root process)

ADF - Fisher Chi-square 21.3734 0.8094 14 56

PP - Fisher Chi-square 32.6039 0.2506 14 56 ** Probabilities for Fisher tests are computed using an asymptotic Chi

-square distribution. All other tests assume asymptotic normality.

102

Stationary Test (at 1st difference)

Pool unit root test: Summary

Series: CDSSPREADS_CHINA, CDSSPREADS_HONGKONG,

CDSSPREADS_INDONESIA, CDSSPREADS_JAPAN,

CDSSPREADS_MALAYSIA, CDSSPREADS_PHILIPPINES,

CDSSPREADS_SOUTHKOREA, CDSSPREADS_THAILAND,

CDSSPREADS_VIETNAM, CDSSPREADS_GERMANY,

CDSSPREADS_IRELAND, CDSSPREADS_ITALY,

CDSSPREADS_PORTUGAL, CDSSPREADS_SPAIN

Date: 10/17/14 Time: 15:44

Sample: 2009 2013

Exogenous variables: Individual effects

Automatic selection of maximum lags

Automatic lag length selection based on SIC: 0

Newey-West automatic bandwidth selection and Bartlett kernel

Balanced observations for each test Cross-

Method Statistic Prob.** sections Obs

Null: Unit root (assumes common unit root process)

Levin, Lin & Chu t* -8.64109 0.0000 14 42

Null: Unit root (assumes individual unit root process)

ADF - Fisher Chi-square 65.4128 0.0001 14 42

PP - Fisher Chi-square 63.8797 0.0001 14 42 ** Probabilities for Fisher tests are computed using an asymptotic Chi

-square distribution. All other tests assume asymptotic normality.

103

Pool unit root test: Summary

Series: GDPGROWTH_CHINA, GDPGROWTH_HONGKONG,

GDPGROWTH_INDONESIA, GDPGROWTH_JAPAN,

GDPGROWTH_MALAYSIA, GDPGROWTH_PHILIPPINES,

GDPGROWTH_SOUTHKOREA, GDPGROWTH_THAILAND,

GDPGROWTH_VIETNAM, GDPGROWTH_GERMANY,

GDPGROWTH_IRELAND, GDPGROWTH_ITALY,

GDPGROWTH_PORTUGAL, GDPGROWTH_SPAIN

Date: 10/17/14 Time: 15:48

Sample: 2009 2013

Exogenous variables: Individual effects

Automatic selection of maximum lags

Automatic lag length selection based on SIC: 0

Newey-West automatic bandwidth selection and Bartlett kernel

Balanced observations for each test Cross-

Method Statistic Prob.** sections Obs

Null: Unit root (assumes common unit root process)

Levin, Lin & Chu t* -24.8321 0.0000 14 42

Null: Unit root (assumes individual unit root process)

ADF - Fisher Chi-square 101.795 0.0000 14 42

PP - Fisher Chi-square 131.585 0.0000 14 42 ** Probabilities for Fisher tests are computed using an asymptotic Chi

-square distribution. All other tests assume asymptotic normality.

104

Pool unit root test: Summary

Series: INFLATION_CHINA, INFLATION_HONGKONG, INFLATION_INDON

ESIA, INFLATION_JAPAN, INFLATION_MALAYSIA, INFLATION_PHILIPP

INES, INFLATION_SOUTHKOREA, INFLATION_THAILAND,

INFLATION_VIETNAM, INFLATION_GERMANY, INFLATION_IRELAND,

INFLATION_ITALY, INFLATION_PORTUGAL, INFLATION_SPAIN

Date: 10/17/14 Time: 15:50

Sample: 2009 2013

Exogenous variables: Individual effects

Automatic selection of maximum lags

Automatic lag length selection based on SIC: 0

Newey-West automatic bandwidth selection and Bartlett kernel

Balanced observations for each test Cross-

Method Statistic Prob.** sections Obs

Null: Unit root (assumes common unit root process)

Levin, Lin & Chu t* -13.8433 0.0000 14 42

Null: Unit root (assumes individual unit root process)

ADF - Fisher Chi-square 33.3787 0.2221 14 42

PP - Fisher Chi-square 46.0403 0.0173 14 42 ** Probabilities for Fisher tests are computed using an asymptotic Chi

-square distribution. All other tests assume asymptotic normality.

Pool unit root test: Summary Series: UNEMPLOYMENT_CHINA, UNEMPLOYMENT_HONGKONG, UNEMPLOYMENT_INDONESIA, UNEMPLOYMENT_JAPAN, UNEMPLOYMENT_MALAYSIA, UNEMPLOYMENT_PHILIPPINES, UNEMPLOYMENT_SOUTHKOREA, UNEMPLOYMENT_THAILAND, UNEMPLOYMENT_VIETNAM, UNEMPLOYMENT_GERMANY, UNEMPLOYMENT_IRELAND, UNEMPLOYMENT_ITALY, UNEMPLOYMENT_PORTUGAL, UNEMPLOYMENT_SPAIN Date: 10/17/14 Time: 15:52 Sample: 2009 2013 Exogenous variables: Individual effects Automatic selection of maximum lags Automatic lag length selection based on SIC: 0 Newey-West automatic bandwidth selection and Bartlett kernel Balanced observations for each test Cross- Method Statistic Prob.** sections Obs

Null: Unit root (assumes common unit root process)

Levin, Lin & Chu t* -9.77855 0.0000 13 39

Null: Unit root (assumes individual unit root process)

ADF - Fisher Chi-square 26.3421 0.4444 13 39 PP - Fisher Chi-square 29.5021 0.2887 13 39 ** Probabilities for Fisher tests are computed using an asymptotic Chi -square distribution. All other tests assume asymptotic normality.

105

Pool unit root test: Summary

Series: IMPORT_CHINA, IMPORT_HONGKONG, IMPORT_INDONESIA,

IMPORT_JAPAN, IMPORT_MALAYSIA, IMPORT_PHILIPPINES,

IMPORT_SOUTHKOREA, IMPORT_THAILAND, IMPORT_VIETNAM,

IMPORT_GERMANY, IMPORT_IRELAND, IMPORT_ITALY,

IMPORT_PORTUGAL, IMPORT_SPAIN

Date: 10/17/14 Time: 15:53

Sample: 2009 2013

Exogenous variables: Individual effects

Automatic selection of maximum lags

Automatic lag length selection based on SIC: 0

Newey-West automatic bandwidth selection and Bartlett kernel

Balanced observations for each test Cross-

Method Statistic Prob.** sections Obs

Null: Unit root (assumes common unit root process)

Levin, Lin & Chu t* -22.5145 0.0000 14 42

Null: Unit root (assumes individual unit root process)

ADF - Fisher Chi-square 85.9519 0.0000 14 42

PP - Fisher Chi-square 108.829 0.0000 14 42 ** Probabilities for Fisher tests are computed using an asymptotic Chi

-square distribution. All other tests assume asymptotic normality.

Pool unit root test: Summary Series: CAB_CHINA, CAB_HONGKONG, CAB_INDONESIA, CAB_JAPAN, CAB_MALAYSIA, CAB_PHILIPPINES, CAB_SOUTHKOREA, CAB_THAILAND, CAB_VIETNAM, CAB_GERMANY, CAB_IRELAND, CAB_ITALY, CAB_PORTUGAL, CAB_SPAIN Date: 10/17/14 Time: 15:54 Sample: 2009 2013 Exogenous variables: Individual effects Automatic selection of maximum lags Automatic lag length selection based on SIC: 0 Newey-West automatic bandwidth selection and Bartlett kernel Balanced observations for each test Cross- Method Statistic Prob.** sections Obs

Null: Unit root (assumes common unit root process)

Levin, Lin & Chu t* -8.64352 0.0000 14 42

Null: Unit root (assumes individual unit root process)

ADF - Fisher Chi-square 43.1794 0.0334 14 42 PP - Fisher Chi-square 51.3706 0.0045 14 42 ** Probabilities for Fisher tests are computed using an asymptotic Chi -square distribution. All other tests assume asymptotic normality.

106

Stationary Test (at 2nd

difference)

Pool unit root test: Summary

Series: CDSSPREADS_CHINA, CDSSPREADS_HONGKONG,

CDSSPREADS_INDONESIA, CDSSPREADS_JAPAN,

CDSSPREADS_MALAYSIA, CDSSPREADS_PHILIPPINES,

CDSSPREADS_SOUTHKOREA, CDSSPREADS_THAILAND,

CDSSPREADS_VIETNAM, CDSSPREADS_GERMANY,

CDSSPREADS_IRELAND, CDSSPREADS_ITALY,

CDSSPREADS_PORTUGAL, CDSSPREADS_SPAIN

Date: 10/17/14 Time: 15:57

Sample: 2009 2013

Exogenous variables: None

Automatic selection of maximum lags

Automatic lag length selection based on SIC: 0

Newey-West automatic bandwidth selection and Bartlett kernel

Balanced observations for each test Cross-

Method Statistic Prob.** sections Obs

Null: Unit root (assumes common unit root process)

Levin, Lin & Chu t* -7.48963 0.0000 14 28

Null: Unit root (assumes individual unit root process)

ADF - Fisher Chi-square 115.056 0.0000 14 28

PP - Fisher Chi-square 114.816 0.0000 14 28 ** Probabilities for Fisher tests are computed using an asymptotic Chi

-square distribution. All other tests assume asymptotic normality.

107

Pool unit root test: Summary

Series: GDPGROWTH_CHINA, GDPGROWTH_HONGKONG,

GDPGROWTH_INDONESIA, GDPGROWTH_JAPAN,

GDPGROWTH_MALAYSIA, GDPGROWTH_PHILIPPINES,

GDPGROWTH_SOUTHKOREA, GDPGROWTH_THAILAND,

GDPGROWTH_VIETNAM, GDPGROWTH_GERMANY,

GDPGROWTH_IRELAND, GDPGROWTH_ITALY,

GDPGROWTH_PORTUGAL, GDPGROWTH_SPAIN

Date: 10/17/14 Time: 16:03

Sample: 2009 2013

Exogenous variables: None

Automatic selection of maximum lags

Automatic lag length selection based on SIC: 0

Newey-West automatic bandwidth selection and Bartlett kernel

Balanced observations for each test Cross-

Method Statistic Prob.** sections Obs

Null: Unit root (assumes common unit root process)

Levin, Lin & Chu t* -38.6961 0.0000 14 28

Null: Unit root (assumes individual unit root process)

ADF - Fisher Chi-square 129.664 0.0000 14 28

PP - Fisher Chi-square 131.371 0.0000 14 28 ** Probabilities for Fisher tests are computed using an asymptotic Chi

-square distribution. All other tests assume asymptotic normality.

108

Pool unit root test: Summary

Series: INFLATION_CHINA, INFLATION_HONGKONG, INFLATION_INDON

ESIA, INFLATION_JAPAN, INFLATION_MALAYSIA, INFLATION_PHILIPP

INES, INFLATION_SOUTHKOREA, INFLATION_THAILAND,

INFLATION_VIETNAM, INFLATION_GERMANY, INFLATION_IRELAND,

INFLATION_ITALY, INFLATION_PORTUGAL, INFLATION_SPAIN

Date: 10/17/14 Time: 16:06

Sample: 2009 2013

Exogenous variables: None

Automatic selection of maximum lags

Automatic lag length selection based on SIC: 0

Newey-West automatic bandwidth selection and Bartlett kernel

Balanced observations for each test Cross-

Method Statistic Prob.** sections Obs

Null: Unit root (assumes common unit root process)

Levin, Lin & Chu t* -7.24232 0.0000 14 28

Null: Unit root (assumes individual unit root process)

ADF - Fisher Chi-square 67.8396 0.0000 14 28

PP - Fisher Chi-square 69.6795 0.0000 14 28 ** Probabilities for Fisher tests are computed using an asymptotic Chi

-square distribution. All other tests assume asymptotic normality.

Pool unit root test: Summary Series: UNEMPLOYMENT_CHINA, UNEMPLOYMENT_HONGKONG, UNEMPLOYMENT_INDONESIA, UNEMPLOYMENT_JAPAN, UNEMPLOYMENT_MALAYSIA, UNEMPLOYMENT_PHILIPPINES, UNEMPLOYMENT_SOUTHKOREA, UNEMPLOYMENT_THAILAND, UNEMPLOYMENT_VIETNAM, UNEMPLOYMENT_GERMANY, UNEMPLOYMENT_IRELAND, UNEMPLOYMENT_ITALY, UNEMPLOYMENT_PORTUGAL, UNEMPLOYMENT_SPAIN Date: 10/17/14 Time: 16:07 Sample: 2009 2013 Exogenous variables: None Automatic selection of maximum lags Automatic lag length selection based on SIC: 0 Newey-West automatic bandwidth selection and Bartlett kernel Balanced observations for each test Cross- Method Statistic Prob.** sections Obs

Null: Unit root (assumes common unit root process)

Levin, Lin & Chu t* -14.7336 0.0000 13 26

Null: Unit root (assumes individual unit root process)

ADF - Fisher Chi-square 59.5434 0.0002 13 26 PP - Fisher Chi-square 58.4277 0.0003 13 26 ** Probabilities for Fisher tests are computed using an asymptotic Chi -square distribution. All other tests assume asymptotic normality.

109

Pool unit root test: Summary

Series: CAB_CHINA, CAB_HONGKONG, CAB_INDONESIA, CAB_JAPAN,

CAB_MALAYSIA, CAB_PHILIPPINES, CAB_SOUTHKOREA,

CAB_THAILAND, CAB_VIETNAM, CAB_GERMANY, CAB_IRELAND,

CAB_ITALY, CAB_PORTUGAL, CAB_SPAIN

Date: 10/18/14 Time: 16:16

Sample: 2009 2013

Exogenous variables: None

User-specified lags: 0

Newey-West automatic bandwidth selection and Bartlett kernel

Balanced observations for each test Cross-

Method Statistic Prob.** sections Obs

Null: Unit root (assumes common unit root process)

Levin, Lin & Chu t* -11.2954 0.0000 14 28

Null: Unit root (assumes individual unit root process)

ADF - Fisher Chi-square 89.5322 0.0000 14 28

PP - Fisher Chi-square 88.0058 0.0000 14 28 ** Probabilities for Fisher tests are computed using an asymptotic Chi

-square distribution. All other tests assume asymptotic normality.

Pool unit root test: Summary

Series: IMPORT_CHINA, IMPORT_HONGKONG, IMPORT_INDONESIA,

IMPORT_JAPAN, IMPORT_MALAYSIA, IMPORT_PHILIPPINES,

IMPORT_SOUTHKOREA, IMPORT_THAILAND, IMPORT_VIETNAM,

IMPORT_GERMANY, IMPORT_IRELAND, IMPORT_ITALY,

IMPORT_PORTUGAL, IMPORT_SPAIN

Date: 10/29/14 Time: 15:59

Sample: 2009 2013

Exogenous variables: None

Automatic selection of maximum lags

Automatic lag length selection based on SIC: 0

Newey-West automatic bandwidth selection and Bartlett kernel

Balanced observations for each test Cross-

Method Statistic Prob.** sections Obs

Null: Unit root (assumes common unit root process)

Levin, Lin & Chu t* -16.0536 0.0000 14 28

Null: Unit root (assumes individual unit root process)

ADF - Fisher Chi-square 103.095 0.0000 14 28

PP - Fisher Chi-square 102.962 0.0000 13 26 ** Probabilities for Fisher tests are computed using an asymptotic Chi

-square distribution. All other tests assume asymptotic normality

110

APPENDIX IV

Normality Test

Figure 4.2

The Result of Jarque–Bera Test for Normality

Table Chi Square for the probability of 0.05 (k = 6 ; n = 70 and df = 69)

0

5

10

15

20

25

-300 -200 -100 0 100 200 300 400 500 600 700

Series: Standardized Residuals

Sample 2009 2013

Observations 70

Mean 1.14e-14

Median -22.61085

Maximum 694.1472

Minimum -253.3945

Std. Dev. 136.5552

Skewness 2.514476

Kurtosis 12.87978

Jarque-Bera 358.4595

Probability 0.000000

111

APPENDIX V

Heteroscedasticity Test

Table 4.11

The Result of White Heteroscedasticity Test

White's test for heteroskedasticity

OLS, using 70 observations

Dependent variable: uhat^2

coefficient std. error t-ratio p-value

--------------------------------------------------------------

const 83225.7 69221.9 1.202 0.2350

gdpgrowth -5809.09 8547.24 -0.6796 0.4999

inflation 15838.8 18355.5 0.8629 0.3924

unemployment -17444.6 10634.2 -1.640 0.1073

import -2008.52 1461.40 -1.374 0.1756

cab 8231.88 6920.08 1.190 0.2399

sq_gdpgrowth 126.429 690.376 0.1831 0.8555

X2_X3 -760.834 1420.90 -0.5355 0.5948

X2_X4 -166.497 991.457 -0.1679 0.8673

X2_X5 -16.3650 59.5365 -0.2749 0.7846

X2_X6 1399.39 753.605 1.857 0.0693 *

sq_inflation -576.417 610.114 -0.9448 0.3494

X3_X4 396.706 1629.28 0.2435 0.8086

X3_X5 41.9809 130.135 0.3226 0.7484

X3_X6 -1954.38 1021.74 -1.913 0.0616 *

sq_unemployme 130.098 280.936 0.4631 0.6454

X4_X5 330.567 120.936 2.733 0.0087 ***

X4_X6 -1160.93 626.104 -1.854 0.0697 *

sq_import 3.14821 4.68264 0.6723 0.5045

X5_X6 -49.6438 65.8908 -0.7534 0.4548

sq_cab 141.950 276.939 0.5126 0.6106

Unadjusted R-squared = 0.435551

Test statistic: TR^2 = 30.488563,

with p-value = P(Chi-square(20) > 30.488563) = 0.062315

112

APPENDIX VI

Multicollinearity Test

Table 4.12

The Result of Multicollinearity Test

GDP

INF

UNEMP

IMP

CAB

GDP 1.000000 0.494156 -0.486683 0.056883 0.182503

INF 0.494156 1.000000 -0.254561 0.181292 -0.080442

UNEMP -0.486683 -0.254561 1.000000 -0.242006 -0.384453

IMP 0.056883 0.181292 -0.242006 1.000000 0.283485

CAB 0.182503 -0.080442 -0.384453 0.283485 1.000000

113

APPENDIX VII

Autocorrelation Test

Weighted Statistics

R-squared 0.317519 Mean dependent var 104.7162

Adjusted R-squared 0.264200 S.D. dependent var 143.4075

S.E. of regression 123.0132 Sum squared resid 968463.2

F-statistic 5.955104 Durbin-Watson stat 1.747141

Prob(F-statistic) 0.000139

Table 4.13

The Result of Autocorrelation Test

using Durbin-Watson Statistic

DW count

du table

value

4 – du

dL table

value

4 – dL

1.747141 1.8025 2.1975 1.4326 2.5674

Table DW for the probability of 0.05 (k = 6 and n = 70)

114

APPENDIX VIII

Chow Test

Table 4.14

The Result of Chow Test

Cross-section F

F

Chow

F

Table

Probability

F

Probability α

Decision

2.688961 2.36 0.0059 0.05 FEM

Table F for the probability of 0.05 (df1 = 5 and df2 = 64)

Redundant Fixed Effects Tests

Pool: POOL01

Test cross-section fixed effects Effects Test Statistic d.f. Prob. Cross-section F 2.688961 (13,51) 0.0059

Cross-section Chi-square 36.541095 13 0.0005

115

APPENDIX IX

Hausman Test

Correlated Random Effects - Hausman Test

Pool: POOL01

Test cross-section random effects

Test Summary Chi-Sq. Statistic Chi-Sq. d.f. Prob.

Cross-section random 5.698829 5 0.3366

Table 4.15

The Result of Hausman Test

Cross-section

RE

Probability

α

Decision

prob

prob

0.3366 0.05 REM

116

APPENDIX X

The Result of Pooled Least Square

Dependent Variable: CDSSPREADS?

Method: Pooled Least Squares

Date: 11/05/14 Time: 11:45

Sample: 2009 2013

Included observations: 5

Cross-sections included: 14

Total pool (balanced) observations: 70 Variable Coefficient Std. Error t-Statistic Prob. GDPGROWTH? -2.525085 5.131544 -0.492071 0.6243

INFLATION? 23.74566 6.753735 3.515930 0.0008

UNEMPLOYMENT? 16.19515 2.265469 7.148695 0.0000

IMPORT? 0.289491 0.343718 0.842236 0.4027

CAB? -9.021021 3.980338 -2.266396 0.0268 R-squared 0.385137 Mean dependent var 183.7091

Adjusted R-squared 0.347299 S.D. dependent var 177.8623

S.E. of regression 143.6947 Akaike info criterion 12.84201

Sum squared resid 1342132. Schwarz criterion 13.00262

Log likelihood -444.4703 Hannan-Quinn criter. 12.90580

Durbin-Watson stat 1.273405

117

APPENDIX XI

The Result of Fixed Effect Model

Dependent Variable: CDSSPREADS?

Method: Pooled Least Squares

Date: 11/05/14 Time: 11:48

Sample: 2009 2013

Included observations: 5

Cross-sections included: 14

Total pool (balanced) observations: 70 Variable Coefficient Std. Error t-Statistic Prob. C 144.8912 193.3279 0.749459 0.4570

GDPGROWTH? 2.780549 6.570028 0.423217 0.6739

INFLATION? 35.09743 9.522458 3.685753 0.0006

UNEMPLOYMENT? 17.79789 13.44986 1.323277 0.1916

IMPORT? -3.093760 3.154304 -0.980806 0.3313

CAB? -6.931212 6.414058 -1.080628 0.2849

Fixed Effects (Cross)

_CHINA--C -152.6706

_HONGKONG--C 417.4084

_INDONESIA--C -214.8675

_JAPAN--C -69.43766

_MALAYSIA--C 114.6618

_PHILIPPINES--C -144.3650

_SOUTHKOREA--C -36.09307

_THAILAND--C 94.68135

_VIETNAM--C -20.61695

_GERMANY--C -77.21711

_IRELAND--C 243.5130

_ITALY--C -40.70270

_PORTUGAL--C 147.2181

_SPAIN--C -261.5120 Effects Specification Cross-section fixed (dummy variables) R-squared 0.650264 Mean dependent var 183.7091

Adjusted R-squared 0.526828 S.D. dependent var 177.8623

S.E. of regression 122.3470 Akaike info criterion 12.67779

Sum squared resid 763408.3 Schwarz criterion 13.28809

Log likelihood -424.7226 Hannan-Quinn criter. 12.92021

F-statistic 5.268017 Durbin-Watson stat 2.189865

Prob(F-statistic) 0.000001

118

APPENDIX XII

The Result of Random Effect Model

Dependent Variable: CDSSPREADS?

Method: Pooled EGLS (Cross-section random effects)

Date: 10/18/14 Time: 17:17

Sample: 2009 2013

Included observations: 5

Cross-sections included: 14

Total pool (balanced) observations: 70

Swamy and Arora estimator of component variances

Variable Coefficient Std. Error t-Statistic Prob.

C 35.74421 61.98233 0.576684 0.5662

GDPGROWTH? -2.531825 5.504267 -0.459975 0.6471

INFLATION? 26.14017 7.410467 3.527466 0.0008

UNEMPLOYMENT? 14.37829 4.754548 3.024112 0.0036

IMPORT? -0.068843 0.551008 -0.124939 0.9010

CAB? -8.353070 4.278162 -1.952491 0.0553

Random Effects (Cross)

_CHINA--C -19.30170

_HONGKONG--C -38.35856

_INDONESIA--C -53.15451

_JAPAN--C 5.896403

_MALAYSIA--C 43.26117

_PHILIPPINES--C -32.10365

_SOUTHKOREA--C -12.18205

_THAILAND--C 28.62729

_VIETNAM--C -17.33986

_GERMANY--C -34.99709

_IRELAND--C 103.0139

_ITALY--C 15.00702

_PORTUGAL--C 122.3926

_SPAIN--C -110.7610 Effects Specification

S.D. Rho

Cross-section random 78.86877 0.2936

Idiosyncratic random 122.3470 0.7064 Weighted Statistics

R-squared 0.317519 Mean dependent var 104.7162

Adjusted R-squared 0.264200 S.D. dependent var 143.4075

S.E. of regression 123.0132 Sum squared resid 968463.2

F-statistic 5.955104 Durbin-Watson stat 1.747141

Prob(F-statistic) 0.000139

119

Unweighted Statistics

R-squared 0.392829 Mean dependent var 183.7091

Sum squared resid 1325341. Durbin-Watson stat 1.276684

a. Dependent Variable: Credit Default Swap Spreads

120

APPENDIX XIII

Partial t-Test

Table 4.16

The Result of t-Test

Note: *Significance level at 10%

** Significance level at 5%

*** Significance level at 1%

a. Dependent Variable: Credit Default Swap spreads

Variable Coefficient Std. Error t-Statistic Prob.

C 35.74421 61.98233 0.576684 0.5662

GDPGROWTH? -2.531825 5.504267 -0.459975 0.6471

INFLATION? 26.14017 7.410467 3.527466 0.0008***

UNEMPLOYMENT? 14.37829 4.754548 3.024112 0.0036***

IMPORT? -0.068843 0.551008 -0.124939 0.9010

CAB? -8.353070 4.278162 -1.952491 0.0553*

121

APPENDIX XIV

Simultaneous F-Test

Table 4.17

The Result of F-Test

a. Predictors: constant, GDP, INF,UNEMP, IMP, CAB

b. Dependent variable: Credit Default Swap spreads

Weighted Statistics

R-squared 0.317519 Mean dependent var 104.7162

Adjusted R-squared 0.264200 S.D. dependent var 143.4075

S.E. of regression 123.0132 Sum squared resid 968463.2

F-statistic 5.955104 Durbin-Watson stat 1.747141

Prob(F-statistic) 0.000139

122

APPENDIX XV

Adjusted R2

Table 4.17

The Result of adjusted R2

a. Predictors: constant, GDP, INF,UNEMP, IMP, CAB

c. Dependent variable: Credit Default Swap spreads

Weighted Statistics

R-squared 0.317519 Mean dependent var 104.7162

Adjusted R-squared 0.264200 S.D. dependent var 143.4075

S.E. of regression 123.0132 Sum squared resid 968463.2

F-statistic 5.955104 Durbin-Watson stat 1.747141

Prob(F-statistic) 0.000139