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An Analysis of Emerging Market
Diversification for an Irish Investor
Shane O’Doherty
MSc Finance & Capital Markets 2011
An Analysis of Emerging Market Diversification
for an Irish investor
Shane O’Doherty (BBS in Business and Finance)
Dublin City University Business School
Dublin City University
Supervisor: Dr Alex Eastman
Course Director: Dr Valerio Poti
MSc Finance & Capital Markets July 2011
Declaration
I hereby certify that this material, which I now submit for assessment on the programme of
study leading to the award to Master of Science in Finance and Capital Markets, is entirely
my own work, and has not been taken from the work of others, save and to the extent that
such work has been cited and acknowledged within the text of my work.
Signature:
Date:
Acknowledgements
First and foremost I would like to dedicate this research article to my mother and thank her
for her help and support during the last year.
I would also like to thank my fellow students and classmates whose assistance and moral
support throughout this difficult year was invaluable.
Finally I would like to thank Dr. Alex Eastman and Prof. Liam Gallagher who work in the area
of Economics, Finance and Entrepreneurship in Dublin City University for their guidance and
support during this research paper.
Abstract
The benefits of International Diversification have been recognized for decades. Since 1981
when the IFC made accurate information pertaining to Emerging Markets their popularity
has increased dramatically.
In this paper I investigate contemporary risk, return characteristics of Developed and
Emerging Markets. I also examine whether favourable correlations still exist between
Developed and Emerging Markets taken from the perspective of an Irish investor. Finally, I
construct two portfolios denominated in Ireland. One consisting of only Developed Markets
Indexes, and the other composed of Developed and Emerging Market indexes. I then
compare the portfolios in terms of the return and risk they offer the Irish investor.
All calculations were based on markets price indexes taken from 11 Developed Market
countries and 22 Emerging Market countries from Bloomberg. The data set chosen was a 15
year time horizon from 1995 – 2010. Three sub-periods were also tested in order to identify
trends. These were from 1995 – 1999, 2000 – 2006 and from 2007 – 2010.
Table of Contents
Page
Chapter 1 Introduction 1
Chapter 2 Literature Review 5 2.1 Diversification 6 2.2 International Diversification 12 2.3 Emerging Markets Diversification 18
Chapter 3 Research Methodology 35 3.1 Hypothesis 36 3.2 Data Description 37 3.3 Relevant Formulae 40
Chapter 4 Data Analysis 44 4.1 Risk, Return, Periodic Growth 45 4.2 ISEQ Correlations 54 4.3 Irish Portfolios 67
Chapter 5 Empirical Findings 74
Conclusions 84
Appendices 88
Bibliography 111
1
Chapter 1
Introduction
2
The most important issue for any investor is the risk and return that an investment presents.
This is whether the investor is investing in a small number or a very large number of assets.
An intrinsic concept in portfolio construction is diversification. The investor can diversify
domestically among different assets and between different industries. International
Diversification is favourable in that it allows investors and portfolio managers to improve
portfolio returns while at the same time, reduces risk levels. The investor can further
maximise returns and minimize risk by diversifying investments into Emerging Markets as
well as Developed Markets.
The World Bank’s current definition of an Emerging Market is a country that has a gross
national income (GNI) of $11,456 or less per capita. An Emerging Market country can be
defined as a society transitioning from a dictatorship to a free market-oriented economy,
with increasing economic freedom, gradual integration within the global marketplace, an
expanding middle class, improving standards of living and social stability and tolerance, as
well as an increase in cooperation with multilateral institutions. According to Forbes, by this
definition, an analysis of all 192 country-members of the U.N. leads to the selection of 81
countries that can be categorized as Emerging Markets. The role of emerging market
countries in the world is now difficult to overestimate. The territory of these countries
occupies 46% of the earth's surface, with 68% of the global population. These economies
account for nearly half of the gross world product.
The term Emerging Markets was coined by economists at the International Finance
Corporation (IFC) in 1981, when the group was promoting the first mutual fund investments
in developing countries and formulated the Emerging Markets Database (EMDB). Since then,
references to Emerging Markets have become ubiquitous in the media, foreign policy and
3
trade debates, investment fund prospectuses and multinationals' annual reports. Up until
the formulation of this database, investment in Emerging Markets had been considered
unfavourable. This is most likely due to the fact that the information that existed prior to
the EMDB was thought to be very unreliable and distorted.
The last 15 years has seen considerable instability in the world economy. In terms of
Emerging Economies there was the Mexican Peso Crisis in 1994 and the Asian Crisis which
began with the devaluation of the Thai Baht in 1996. Following closely to this Russia went
through its own financial crisis in 1998. Pertaining to developed economies then the period
during the 1990’s was a strong bull market which came about due to sudden and dramatic
improvements and innovations in technology. The period at the turn of the millennium saw
this bull market come to something of a climax with the “Dot Com Bubble”. Following this
the world economy slowed down exhibiting a period of a more bearish nature. With the
world economy emerging from a serious financial crisis that began in 2007 the outlook for
the majority of developed economies is bearish. Investors will look to minimize risk levels of
portfolios in any way they can and many will look to investment in Emerging Markets as an
opportunity to reduce risk.
The opening of these large economies to global capital, technology, and talent over the past
two decades has fundamentally changed their economic and business environments. As a
result, the GDP growth rates of these countries have dramatically outpaced those of more
developed economies, lifting millions out of poverty and creating new middle classes and
vast new markets for consumer products and services. Large, low-cost and increasingly
educated labour pools, meanwhile, give these markets tremendous competitive advantage
4
in production, and information technology is enabling companies to exploit labour in these
markets in unique ways.
For my research article I will look at the risk return characteristics for the Emerging Markets
and compare them with those characteristics shown by Developed Markets. I will also
examine the correlations between 11 Developed Market indexes and 22 Emerging Market
indexes. In this section I will look primarily at correlations from the perspective of the Irish
investor. I will also closely examine correlations for the S&P 100 with the other 32 test
indexes for comparison and to increase the validity of my findings. For the final section of
my investigation I look at two different portfolio types for an Irish investor. The first
portfolio consists of only DM indexes, while the second includes both DM and EM indexes. I
compare the two portfolios based on the risk and returns they present. For each of the
three sections of my analysis I look at data from the 15 year period 1995 – 2010. I also
calculate results for three sub-periods from 1995 – 1999, 2000 – 2006 and from 2007 –
2010. This was done to see whether there any trends evident over the time horizon.
In my literature review I look in considerable detail into the history and theory behind the
idea of diversification, international diversification and diversification into Emerging
Markets. In my research methodology chapter I will outline my hypothesis and give a
description of the data and formulae I used. For my data analysis I will outline the important
results that I found in my research. In Chapter 5 I will discuss my empirical findings. In this
chapter I will link the results obtained from my research with previous findings from my
literature review. The empirical findings shall also include minor limitations that my
research may have been subject to and I will recommend areas where I believe future
research should be beneficial for the Irish investor.
5
Chapter 2
Literature Review
6
2.1 – Diversification
“Diversification is both observed and sensible; a rule of behaviour which does not imply the
superiority of diversification must be rejected both as a hypothesis and as a maxim.”
(Markowitz 1952)
It was not until 1952 that Harry Markowitz published a formal model of portfolio selection
embodying diversification principles. In his work Markowitz drew attention to the common
practice of portfolio diversification and showed exactly how an investor can reduce the
standard deviation of portfolio returns by choosing stocks that do not move exactly
together. Markowitz proposed that investors should focus on portfolios based on their
overall risk return characteristics. Markowitz was by no means the first to consider the
potential benefits from diversification. He refers to Bernoulli’s article in 1738 as one of the
influences of his work. Markowitz had the brilliant insight that, while diversification would
reduce risk, it would not generally eliminate it. Markowitz's paper is the first mathematical
formalization of the idea of diversification of investments.
Probably the most important aspect of Markowitz's work was to show that it is not a
security's own risk that is important to an investor, but rather the contribution the security
makes to the variance of his entire portfolio (Rubenstein 2002). This was primarily a
question of its covariance with all the other securities in his portfolio. Where previous
theory concentrated more on an individual security analysis, and did not account for
correlations of risk between assets.
7
“What was lacking prior to 1952 was an adequate theory of investment that covered the
effects of diversification when risks are correlated”
(Markowitz 1999)
Markowitz also added the brilliant insight that, while diversification would reduce risk, it
would not generally eliminate it. The risk that remains even after extensive diversification is
called market risk. This type of risk is also called systematic or non-diversifiable. The risk
that can be eliminated through diversification is called firm-specific or non-systematic risk.
Markowitz assumed that the investor would be a mean-variance optimizer in looking for the
optimum “efficient” portfolio. The portfolio is considered as efficient if and only if it offers a
higher overall expected return than any other portfolio with comparable risk (Sharpe 1967).
According to Markowitz’s studies the highest risk return combination is found by finding the
optimal portfolio on the efficient frontier / investment opportunity set of assets. If we treat
single period returns for various securities as random variables, we can assign them
expected values, standard deviations and correlations. In his work in 1952 Markowitz
showed that based on these we can calculate the expected return and volatility of any
portfolio constructed with those securities. Essentially this means that we are taking
expected returns and volatility as proxies for risk and reward. If the returns are not
correlated, diversification could reduce risk. On the other hand, if security returns are
perfectly correlated, no amount of diversification can affect risk.
In order to simply convey how the expected return on a portfolio might be attained under
Markowitz’s model we will take an example where an individual’s wealth is invested in 2
8
assets. A proportion denoted W1 is invested in the first asset, and the remainder of 1 – W1,
denoted W2 is invested in the second asset. The expected return on the portfolio denoted
ERP can then be found by getting the weighted average of the expected return on the
individual securities (ER1) and (ER2). Such that:
1) ERP = W1 (ER1) + W2 (ER2)
The next central factor to Markowitz’s optimal portfolio selection is to find the standard
deviation of the portfolio. However to do this the co-variances between the individual
assets must be found as has been mentioned. In continuity with our basic case the
covariance between asset 1 and asset 2 is found by:
2) Cov12 = P (r1 – ER1) (r2 – ER2)
The new factors r1 and r2 that have been introduced here represent the actual returns on
the assets. The probability of the scenario is included by the factor “P”. The result from the
covariance equation conveys the degree to which the assets’ returns move in tandem with
each other. For diversification benefits we would here be looking for the assets that give the
lowest covariance readings to minimize the risk level of the portfolio.
The benefits of a low covariance of returns of the individual securities can be best
highlighted by Markowitz’s formula for attaining the variance of a portfolio:
3) σP2 = W1
2 σ12 + W2
2 σ22 + 2 W1W2 Cov12
9
From this formula where σ12 and σ2
2 are the variances of the individual securities, we can
clearly see that a low covariance between securities 1 and 2 will directly result in a lower
portfolio variance and therefore standard deviation i.e. the portfolio benefits from
diversifying into different securities. It should also be noted that another method by which
the covariance between securities can be attained is by using the correlation coefficient
such that:
4) Cov12 = p12 σ1 σ2
In the above equation p12 represents the correlation coefficient. Markowitz’s 1952 paper
seems to contain the first occurrence of this equation in a published paper on financial
economics (Rubenstein 2002). In this model the correlation can be anywhere from -1 to +1.
Where the more the correlation is negative the smaller the co-variance will be and
therefore the smaller the level of risk there is in the overall portfolio. The combination of
risk and return on a portfolio is subject to the preferences of the individual investor.
As is realistically the case investors will generally have large numbers of assets to be
measuring. In this case a variance-covariance matrix would be used to generate a standard
deviation for the portfolio. As has been mentioned the variance of the portfolio is the
weighted sum if co-variances, ad each weight is the product of the portfolio proportions of
the pair of assets in the covariance term.
The bordered variance-covariance matrix has the portfolio weights for each asset placed on
the borders. To find portfolio variance, multiply each element in the covariance matrix by
10
the pair of portfolio weights in its row and column borders and add up the resultant terms.
If there were only two assets we would get equation 2 as the result. If there are a number of
assets the matrix would look as follows: (where σ12 denotes covariance for clarification
purposes)
Weights w1 w2 w3 w4 wn
w1 σ11 σ12 σ13 σ14 σ1n
w2 σ21 σ22 σ23 σ24 σ2n
w3 σ31 σ32 σ33 σ34 σ3n
w4 σ41 σ42 σ43 σ44 σ4n
wn σn1 σn2 σn3 σn4 σnn
12 years on from Markowitz’s portfolio selection breakthrough, Sharpe, Lintner and Mossin
developed a model that conveyed individual asset risk premiums as a function of asset risk
(Sharpe 1964). Under this new model, the relevant measure of risk for individual assets held
as part of well diversified portfolios is not the assets standard deviation or variance; it is
instead the contribution of the asset to the portfolio’s variance which is measured by the
beta of the asset.
B1 = Cov (R1, RM) / σM2
In this case the assumption is taken that the mean variance optimal portfolio is considered
as being the relevant market portfolio where RM is the return on the market portfolio and
σM2 is the variance of the market portfolio. The Beta co-efficient “B1” of a security is defined
as the extent to which return on the stock and returns on the market move together (Bodie,
11
Kane, Marcus 2010). The expected return beta relationship is the most recognized
expression of the CAPM:
ER1 = rf + B1 (ERM – Rf)
An important factor in the above equation is the introduction of the option to invest in a
riskless asset “rf”. The option for the investor to lend or borrow at the risk free rate means
that there will be no covariance element as was seen in Markowitz’s model. In the above
equation the factor “ERM – Rf” represents the risk premium or the market price of risk. That
is that it quantifies the extra return that investors demand to bear portfolio risk.
The single index model, CAPM predicts that only one type of non-diversifiable risk influences
expected security returns. That single type of risk is the “market risk”. Stephen Ross
developed a new theory only about a decade after the CAPM was founded. This was the
multi-index model, the APT, which is more general in that it accounts for a variety of
different economic risk sources. The APT provides a portfolio manager with a variety of new
and easily implemented tools to control risks and to enhance portfolio performance
(Burmeister, Roll, Ross 1994).
Several of these economic variables were found to be significant in explaining expected
stock returns, most notably, industrial production, changes in the risk premium, twists in the
yield curve, and, somewhat more weakly, measures of un-anticipated inflation and changes
in expected inflation during periods when these variables were highly volatile (Chen, Roll,
Ross 1986). These modern studies have found that the multifactor APT approach has far
greater explanatory power than the CAPM.
12
2.2 - International Diversification
From the principles learned from the development of Markowiz’z portfolio theory, in the
early 1970s experts began to highlight the potential advantages from internationally
diversifying a portfolio.
“The international diversification of portfolios is the source of an entirely new kind of world
welfare gains from international economic relation” – (Grubel 1968)
The first empirical literature on the benefits of international diversification was developed
by Grubel where he looked at ex post realized rates of return from investment in 11 major
stock markets of the world. In 1970, Levy and Sarnat underwent a more comprehensive
study dedicated primarily to looking at international diversification of investment portfolios.
In order to convey the potential gains from diversification they looked at data from 1951 –
1967 using rates of return from 28 different countries.
Levy and Sarnat also highlighted the optimum portfolio by using the market equilibrium
model (Lintner 1965). What was perhaps the most striking feature of Levy and Sarnat’s
paper was the fact that there were considerable benefits to be gained from using
developing countries as part of the optimal portfolio. Their results showed that the higher
the number of countries that were invested in and the more regions that were taken into
consideration, meant the more favourable the risk return combination of the portfolio. The
empirical results from this test were highly significant. The best combination that can be
created out of equities in the developing countries is a portfolio with a 5% return and a
13
26.5% standard deviation as compared with a return of 12% and standard deviation of 8%
for the optimum portfolio which included all countries. Levy and Sarnat estimated that the
benefits of diversification could be further improved by removing barriers to international
flows of capital. This theory was empirically proved by Lessard in 1973 by using his
Investment Union concept.
“Complete freedom of international capital movements would provide investors with a
maximum opportunity for diversification” – (Lessard 1973)
In his work in 1974 Bruno Solnik focused on highlighting the benefits from risk reduction
with differing amounts of stocks in portfolios. He also compared risk levels of solely
domestic portfolios with internationally diversified ones. Solnik’s empirical results also
showed that the marginal reduction in standard deviation achieved from additional stocks in
the portfolio decreased quite rapidly. He showed that an American investor holding 20
securities reduces his total risk by only another 3% if he added another 50 different
securities to his domestic portfolio. Solnik highlighted the fact that despite how many
securities that are added to the portfolio, there will always be an element of risk remaining.
This is the systematic/market risk when investing in domestic securities alone, which was
mentioned earlier, was shown by Solnik to have considerable reduction potential if the
investor was to diversify internationally. It was found that in the case of the US the
variability in return of an international well diversified portfolio would be only one tenth as
risky as a typical security and half as risky as a portfolio of well diversified purely US stocks.
14
These results could have been even more exaggerated if developing market securities had
been included.
In 1976 Lessard explored the effects that taxation, transaction costs, currency controls and
fluctuations in exchange rates may have on international investment. In his findings Lessard
sought to explain the covariance structure of equity returns in international markets. He
looked at whether it was country or industry factors that dominated and he aimed to
convey if the gains from international diversification of markets are assumed to be
integrated or segmented using two sets of data. The first is monthly percentage changes in
market-value weighted price indexes for 16 countries and for 30 industries covering the
period January 1959 to October 1973. The second is monthly price changes for 205
individual securities from 14 countries and 14 industries for the period January 1969 to
October 1973. Lessard’s finding supported previous work by Grubel, Levy, Sarnat and Solnik.
“Country factors are the most important elements in the covariance structure, reinforcing
the view that the international dimension is particularly critical in reducing risk through
diversification.” – (Lessard 1976)
After finding that it was indeed country factors that dominated the nature of the covariance
structure Lessard found that the magnitude of these gains will depend, however, on
whether markets are segmented or integrated internationally. Lessard found that if markets
are integrated, the benefits of international diversification may be overstated. This is partly
because a few large countries represent the bulk of the market value and the risk elements
of these countries will contribute prominently to the world market portfolio. If markets are
15
segmented, on the other hand, then a more complete diversification of country effects
should be beneficial. It was concluded that the new risks introduced by Lessard were
outweighed by the benefits attributable to international diversification for the investor. In
his work Lessard also points to the fact that investors tend to not diversify internationally to
a theoretically efficient extent. This is idea was to be examined further in years to come.
It was pointed out by Lessard in his work in 1976 that although there are inherent
gains available to investors who diversify internationally; the evidence is showing that the
majority of investors are not efficiently using this opportunity. In contrast to the previous
work on the subject of international diversification by Levy, Sarnat and Lessard, in the early
1990’s experts began to look at investor choices rather than institutional constraints as the
reason that international diversification not occurring to its efficient level. In 1991 French
and Porterba found that over 98% of Japanese equity portfolios were held domestically by
investors. Analogous figures of 94% and 82% were found for the US and the UK respectively.
In order to measure the costs associated with incomplete diversification, French and
Porterba calculate the expected returns implied by the actual portfolio holdings of US,
Japanese and UK investors. They then compute the expected returns implied by an
international value weighted portfolio strategy for investors in each nation and compare the
results. In their empirical findings it was discovered that UK investors must expect annual
returns in the UK market more than 500 basis points above those in the US markets to
explain their 82% investment in domestic shares. Analogous figures for the US, Japan
relationship and the Japan US relationship were 250 and 350 basis points respectively.
These results show that investors expect domestic returns that are systematically higher
16
than those implied by a diversified portfolio. French and Porterba then sought to empirically
investigate whether it was institutional factors or investor choices that were to be
attributed as being the primary reason for imperfect international diversification.
The institutional reasons for the existence of this concept of imperfect international
diversification that were tested were the effect of taxes, transaction costs, market liquidity,
cross border equity flows and government limitations to cross border investment. Empirical
tests on the above factors were found to be insignificant in negatively affecting the degree
of international diversification. French and Porterba therefore suggested a different
reasoning.
“Because constraints on foreign holdings are not binding, this implies that incomplete
diversification is the result of investor choices” - (French & Porterba 1991)
The second potential factor tested that might cause imperfect international diversification
focuses on investor behaviour. With one important possibility being that return
expectations may vary systematically across groups of investors. In a study between the US
and Japanese investors, empirical evidence showed that while Japanese investors were
more optimistic than their US counterparts with respect to both markets, they were
relatively more optimistic about the Tokyo market. In terms of risk it was found that
investors tend to attribute extra risk to foreign investments because they know less about
foreign markets, institutions and firms.
17
As was pointed out previously it was found by Lessard (1976) that there are higher
diversification benefits to be gained when international markets are segmented than when
they are integrated. The early 1990’s saw growth in international investment which
paralleled growth in international financial market integration. National economies also
appeared to be becoming more dependent on the world business cycle (Odier, Solnik 1993).
This prompted Odier and Solnik to test whether the international diversification was still
beneficial from a risk return viewpoint. They look at what has changed over a 20 year period
and the implications of the changes for international investment.
In their findings it was discovered that asset allocation between equities, fixed income
securities and cash and cash equivalents were the major factor to the performance and risk
of a portfolio. They found that 90% of the monthly variation on returns on a large sample of
mutual funds was explained by asset allocation while only 10% was determined by security
selection. It was found that correlations between major nations increased as global market
volatility increased, which is exactly when one would hope that the benefits of low
correlations from diversification would be recognized. However, even if the correlation
between markets is increasing slightly, it remains quite low because of the relative
independence of national economies and monetary policies. Odier and Solnik concluded
that even though the international environment changes over time, efficient international
asset allocation strategy opportunities can be identified using careful research.
18
2.3 – Diversification in Emerging Markets
It was pointed out by Solnik in reflection of his work in 1974 that a study of the
inclusion of developing markets into portfolios could further add to the potential
opportunities and efficiency for internationally invested portfolios. The term Emerging
Markets (EM) was coined by economists such as Antoine W. Van Agtmael at the
International Finance Corporation (IFC) of the World Bank in 1981, when the group was
promoting the first mutual fund investments in developing countries (Forbes). It was
pointed out by Errunza (1983) that the research on international diversification carried out
up until that point was stopping short of a truly efficient global portfolio. He said that this
was the case because of the fact that previous research had been limited to the securities
markets of developed countries. Up until 1983 there was very little investment to be seen in
EMs. Errunza attributed this to the fact that there was very little information available about
the markets and where there was information it would likely have been unreliable.
In order to address this lack of information pertaining to EMs, the IFC created a new data
bank consisting of broad market-wide statistics on 15 EMs and security-specific return data
for the period 1976-80 from nine EMs. This new data base provided investors with their first
real opportunity to compare EMs with developed markets (DMs) using reliable data on
heavily traded individual securities. Using this data from the IFC databank Errunza found
that the returns on EMs were generally high relative to industrialized countries. It was
pointed out in his paper also that the benefits of internationally diversifying a portfolio
among purely DMs were eroding somewhat in the years approaching 1983. Errunza also
reported on the correlations between EMs and DMs for the period 1976 – 1980. The first
empirical finding was that there were relatively high correlations amongst the DMs. The
19
results also showed that portfolio risk could be reduced substantially by including EMs in
diversified portfolios. Furthermore, the correlations between EMs are also low in
comparison to the correlations displayed by members of the European or North American
blocs.
As was highlighted by Lessard and Solnik in their research earlier, there is a significant
national factor in security returns, implying limits to risk reduction through domestic
diversification. Since security returns across countries are less than perfectly positively
correlated, however, a large part of the national systematic risk is diversifiable in the global
context. In a sample including 15 DMs and 12 EMs, Errunza also sought to explain the
proportion of domestic market return variance that could be explained by alternate world
indexes. The empirical findings showed similar results as previous research for DMs. Errunza
discovered that the proportion of variance explained by the world factor is extremely small
for EMs suggesting definitive potential benefits from holding a truly global portfolio.
As has been outlined already there are a number of barriers pertaining to international
investment. Errunza discussed the relative importance of each of the different types to
investing in EMs. Firstly he looked at currency risk, whether fluctuations in exchange rates
could unfavourably affect the real returns to investors in EMs. It was reported that the
realized returns reported here did not increase volatility or reduce security returns to
unacceptable levels. Therefore regarding investment in EMs currency risk should not be an
issue for investors with well diversified portfolios who are looking to invest in EMs. The
importance or political risk associated with EMs such as expropriations, nationalizations or
capital controls was found to depend on the risk aversion /opportunity set of the investor
and how well the EM markets in question were functioning. Tests performed on the IFC
20
sample securities suggest that EMs are almost as efficient as European markets. Some
countries can have restrictions on capital flows across their borders however the majority of
EMs had little or no restrictions on capital flows, and the ones that did were either
loosening legislation or might remove barriers in the future. In most cases it was found that
the tax treatment of repatriated dividends and capital gains was similar to that of policies in
DMs. Errunza concluded that the typical barriers to international investment did not have a
significant effect on the benefits of internationally diversifying using EM securities. Errunza
did point to the potential danger to the investor of differing policies in EMs regarding
financial reporting that might require special knowledge and interpretation skills for cross
country comparisons.
In a later work Errunza (1988) like previous experts pointed to the fact that the
average international portfolio manager remains very hesitant about investing in emerging
markets. A major concern may be the impact of global recession and the debt problems that
plagued many emerging markets during the early 1980s. Errunza sought to investigate
whether these major shocks have an effect on the performance of emerging markets. The
data for his research in this journal article covered the period from 1976 – 1984 and
included more EM countries due to ever increasing data transparency. There was also the
effect of currency fluctuations between EMs and DMs on diversified portfolios to consider,
associated with the period of financial distress in the early 80’s.
The empirical findings showed that despite the global recession, the performance of
emerging markets over the 1976 - 1984 period was consistent with that reported for the
earlier period between 1976 - 1980. Furthermore with respect to the benefits of
diversification, the emerging markets actually displayed a lower correlation with developed
21
markets over the 1981-84 periods than over the 1976 - 1980 period. As was the case with
previous studies as well, given the long-term nature of investments in emerging markets,
and the fact that any global portfolio would invest reasonably small amounts in emerging
markets, the currency fluctuation problem is not critical in terms of its effect on overall
portfolio diversification.
An in depth quantitative analysis of EMs was developed by Divecha, Drach and
Stefek in 1992. In their research they aimed to develop a model that would shed light on the
forces that drive EMs. This would help investors make better informed decisions to avail of
international diversification using EMs. In tandem with the previous empirical research
(Errunza 1983, 1988), they found that EMs are more volatile than DMs. It was also conveyed
that EMs have relatively low correlations amongst each other, and that there were low
correlations between EMs and DMs. These low correlations highlight the opportunity for
diversification associated with the addition of EM securities to an international portfolio.
The data that was selected for this analysis was taken from the IFC and consisted of 23 EM
countries, as well as the US, UK and Japan. The sample period covered from February 1986 –
July 1991.
In their analysis it was seen that homogeneity amongst securities within a given EM was
evident. That is to say that all stocks within a given EM are very sensitive to changes in the
given country’s market index. One could say that individual stocks in EMs have high Betas
with the market portfolio, more so anyway than in DMs. In the second part of their research
they looked at correlations across EMs and discovered a significant degree of heterogeneity.
EMs were seen to be considerably less correlated with each other than the DMs were. The
22
analysis highlighted an average correlation amongst EMs as low as 0.07, meaning they are
almost uncorrelated.
The implications from the point of view of an investor from this study are that there are
considerable diversification benefits to be gained from investing in EM indexes. In their
analysis they conveyed that over the sample horizon, a global investor who allocated 20% of
their wealth in an EM composite index fund and 80% in DMs would have reduced their
overall annual portfolio risk by 0.81%, while simultaneously increasing annual return by
2.1%. This is in comparison to a portfolio with a 100% allocation in DMs.
An analysis of risk and returns and their predictability in emerging markets was
researched by Campbell R. Harvey (1994). Using data from the Emerging Markets Database
(EMDB) and the IFC he provided the first comprehensive analysis of risk and return in EMs
and the effect of their inclusion in a diversified portfolio. The data included 20 EM nations
from Europe, Latin America, Asia, Africa and the Middle East, as well as over 800 equities.
The paper had three primary goals. Firstly Harvey sought to study the unconditional risk of
returns of EM securities. Second, he researched why EMs have such high expected returns
and finally the time variation in EM returns was studied.
Where previous authors documented low correlations of the emerging market returns with
developed country returns, Harvey differentiated his study to test whether adding EM
assets to the portfolio problem significantly shifts the investment opportunity set and the
efficient frontier. In his findings it was seen that the addition of EM securities did indeed
enhance the risk return relationship of portfolios. That is that it moved the investment
opportunity set up and to the left.
23
In the second part of Harvey’s study he seeks to explain why the emerging market equities
have high expected returns, when under the framework of asset pricing theory it was found
that exposures to the commonly used risk factors are low for EMs. Applying standard one
and two-factor global asset pricing paradigms leads to large pricing errors. Harvey indicates
this failure may be caused by the fact that under the asset pricing model the assumption
that complete integration of world markets exist might be inaccurate.
Lastly, by studying the time-variation in EM returns, Harvey conveyed that EMs contrast
with DMs in at least two respects. It was shown that EM returns are actually more
predictable than in DMs. Also, unlike in DMs, EM returns are more determined by local
information than by global information. One interpretation derived of the influence of local
information is that the emerging markets are segmented from world capital markets. A
second interpretation is that there is important time-variation in the risk exposures of the
emerging markets.
“For countries with stable, developed industrial structures, many researchers studying time-
varying asset returns have assumed that risk loadings are constant”- (Harvey 1994)
This is a far less reasonable assumption for developing countries. The country risk exposure
reflects the weighted average of the risk exposures of the companies that are included in
the country index. As the industrial structure develops, both the weights and the risk
exposures of the individual companies could change. This may induce time-variation in risk
exposure within the EMs. Harvey concluded that future research should investigate an asset
pricing framework that allows for the possibility of incomplete integration and for the
degree of integration to change through time.
24
Pursuant to previous research discussed, EMs have considerably different features
from DMs. There are four distinguishing features that separate the two. EMs have higher
average returns, correlations with developed market returns are low, returns are more
predictable and volatility is higher (Bekaert, Harvey 1995). In a later study by Bekaert and
Harvey in 1997, they sought to explore cross sectional determinants of investment
strategies in EMs. Following that they examined some of the issues in using EM equity data
such as investability, survivorship and non-normality.
In the research they looked at data from the IFC, Morgan Stanley Capital International
(MSCI) and the ING Barings Emerging Markets Indices (BEMI). The IFC and MSCI both
present two types of indexes, global and investable. While the BEMI only focuses on
investable indexes. It was important to study markets before and after they were made
accessible to international investors. This is because as has been discussed, an intrinsic part
of studying EMs is the impact that capital market liberalizations have on the returns. The IFC
and the MSCI were found to be very similar in the data they present. However the IFC data
was determined as the most favourable due to the fact that it covered the longest history of
data as was therefore the least subject to omitted variable bias.
Following on from their previous work in 1994, in this research they looked at the degree to
which time-varying world market integration impacts on the distribution of returns for EMs.
To convey this they looked at summary annualized EM volatilities and mean returns from
the 1980’s and the 1990’s and compared results from the two periods. Most of the capital
market liberalizations had taken place before 1991. Their results showed that the mean
results in most countries are much lower in the 1990’s than in the 1980’s. An example
25
would be that the four countries that had returns greater than 65% in the 1980’s, all had
returns less than 25% in the 1990’s.
The importance that global integration of capital markets had was also further evidenced, as
the influence of global and local information changes. The results showed the EM
correlations were increasing over time in tandem with the ever increasing integration of
capital markets. However it was also noted there is still more than sufficient diversification
benefit for the investor to avail of. With respect to the Beta measurement of risk, the Beta
coefficients measured in this study conveyed significantly higher readings in the 1990’s than
in the 1980’s. This reflects the fact that EM country returns are becoming more sensitive to
world market returns, further reinforcing the importance of the impact of global market
integration on the benefits that can be gained from international diversification.
The limitations of the CAPM single factor model are also further highlighted in Harvey’s
research in 1997. This is particularly clear with regards to the results from the 1990’s
dataset. In the findings Harvey used the t-statistic to measure the statistical significance of
the results. The higher the t-value, the greater the confidence we have in the coefficient,
Beta as a predictor. Low t-values are indications of low reliability of the predictive power of
that coefficient. The Beta average return appears to be stronger in the 1990’s from first
glance. However there is one factor that is subjecting these findings to considerable error.
Poland was found to have a high return and an extremely high Beta. It was discovered that if
the average return were regressed on the Betas, the t-stat was 3.2. When Poland was
removed from the analysis the t-stat dropped to 0.4. Coinciding with research previously
discussed the failure of the CAPM to explain EM returns could be interpreted in a number of
ways. The benchmark world portfolio may not be mean-variance efficient and perhaps a
26
multifactor representation is more appropriate for EMs. The CAPM therefore based on
these findings is not useful in explaining the cross section of average returns. Instead the
most suitable risk measurement in completely segmented capital markets is the volatility.
Finally Harvey and Bekaert explore a group of risk attributes to EMs. These attributes
included a wide range of country characteristics, some of which included political risk,
inflation, demographics, market integration which were found to be important factors in
investment strategies for EMs. They also found that a number of fundamental attributes
including the International Country Risk Guide’s Composite Risk, trade to GDP and earnings
to price are useful in identifying high and low expected return environments.
Contrary to previous result found pertaining to EM returns, it was found that EMs
did not produce high compound returns relative to US stock markets when a 20 year time
horizon ending in June 1995 was used (Barry, Peavy, Rodriguez 1998). Pursuant to the
empirical research studied here we know that EMs have experienced high levels of volatility,
but they have also provided significant diversification benefits to investors when combined
with DM portfolios. They used data from the IFC's Emerging Markets Data Base (EMDB) to
examine the risk and return characteristics of emerging markets and their diversification
benefits for portfolios based on U.S. stocks.
It was found as expected that EMs as a group portrayed monthly standard deviations of
returns of 5.61%. This was compared to the US equivalents of 4.25% and 5.26% for the S&P
500 and the NASDAQ respectively. These standard deviations were for the period 1975 –
1995 and similar results were derived for the period from 1985 – 1995. Also in tandem with
previous empirical research, the correlation between EM markets and the US market over
the 20 years was 0.34 which conveys the potential gains from diversification for the investor
27
by including EM market indexes in their international portfolio. However there was one
statistical finding that was contrary to previous analyses. That is to say that over the 20 year
period, the EM composite index gave a monthly mean return of 0.99%. Lower than the
returns measured for the S&P 500 and the NASDAQ of 1.11% and 1.07% respectively.
The optimal asset allocations and minimum variance portfolios to these markets were
shown to change from period to period. The minimum variance portfolio for the period
from 1985 – 1995 was shown to involve an allocation of 20% of funds in the EM composite
index and 80% in the US index. Analogous percentages of 50% in both the EM composite
index and the US index for the period from 1976 – 1985 were calculated. Some individual
emerging markets provide especially powerful diversification opportunities for U.S.
domestic investors. For example, allocating 20% of a portfolio to Thai stocks and the
remainder to the S&P 500 would have allowed U.S. domestic investors to earn a higher rate
of return at substantially lower variability than the S&P 500 alone would have given them
during the 1975 - 1995 period. It was also noted that care should be taken before investing
in some of the smaller EMs where there might be less detailed information available.
The ex-post framework utilized in past analyses do not reveal the whole picture for
constructing useful and profitable investment strategies and they potentially overstate the
true level of gains which can be obtained from an emerging market diversification strategy.
They are computed where past averages are substituted for portfolio inputs such as means,
standard deviations and correlations, and on the assumption that, with respect to the inputs
to the portfolio decision, investors are blessed with perfect foresight (Fififield, Power,
Sinclair 2002). In their paper Fififield, Power and Sinclair (FPS) attempt to overcome this
limitation by estimating the ex-ante gains available from investing in EMs. The ex-ante
28
measurement, unlike the ex-post, generates optimal portfolios based on forecasted means,
standard deviations and correlations.
Firstly, FPS show ex-post risk-return advantages of a portfolio which combines UK and EM
securities for the period 1991 – 1996. The findings from this test show that there was
indeed considerable scope for potential, or theoretical, benefits from this particular form of
diversification as previous empirical research had shown. Furthermore, the empirical results
obtained in their analysis suggest that EMs do indeed provide diversification benefits even
during times of crisis, when diversification is most valuable. This was conveyed by the fact
that the Mexican Peso Crisis occurred during the sample period in December 1994 which
caused EMs throughout Latin America to move significantly in a negative direction, and to a
lesser extent EMs worldwide also experiences the effect of the financial crisis.
However, to investigate whether the theoretical gains available from EM diversification can
be achieved in practice FPS applied a simple model to forecast the portfolio inputs of
means, standard deviations and correlations for the period from 1994 - 1996. Ex-ante
MRPUR optimal portfolios were then generated, which is the ratio of its mean return to its
standard deviation following Markowitz (1959). One assumption that was taken was that
investors place greater emphasis on the more recent past when estimating future portfolio
inputs. A key result from the analysis indicated that a strategy based on forecasted means,
standard deviations and correlations, achieved very few of the gains attained in ex-post
analyses of emerging market diversification.
29
“The poor performance of the ex-ante strategies examined pointed overwhelmingly to the
inadvisability of relying on historical data to identify ex-ante, a portfolio that combines the
virtues of a high expected return with a low return volatility” – (Fififield, Power, Sinclair
2002)
It was also noted in their conclusion however that there is recent promising evidence that
indicates a predictable time-varying component in the returns of emerging market shares
which can be exploited for successful investment strategies. Fififield, Power and Sinclair
reflect in their conclusion that there are three future challenges to be addressed. Firstly
there should be further study dedicated to this predictable component for EMs. Second,
forecasting models using longer time horizons should be used. They also point to the fact
that further persistence or predictability in the EM risk-return relationship for investors’
diversified portfolios should be studied.
The following year from this, an analysis was undertaken to assess the effect that the
global scale market liberalization that was taking place had on the volatility of capital flows
in EMs and the performance of investment portfolios. The pioneering studies of Errunza
were largely ignored by the practitioner communities. Nevertheless, interest in emerging
market investments re-surfaced in the early 1990s in tandem with global capital market
liberalizations. Previous empirical research shows very significant diversification benefits for
emerging market investments. These studies as has been mentioned used market indexes
compiled by the IFC. However results generated from IFC data generally ignore the high
transaction costs, low liquidity, and investment constraints associated with EM investments.
Bekaert and Harvey (2003) discuss the measure the diversification benefits from emerging
equity markets using data on closed-end funds (country and regional funds), and American
30
Depository Receipts (ADRs). Unlike the IFC indexes, these assets are easily accessible to
retail investors, and transaction costs are comparable to those for US traded stocks. It was
found that investors generally have to sacrifice a substantial amount of diversification
benefits of investing in foreign markets when they do so by holding closed-end funds. ADRs
and open-end funds on the other hand track the underlying IFC indices much better than
other investment vehicles and prove to be the best diversification instrument. Pursuant to
the empirical research they also found that market liberalizations increased correlations
between EMs and DMs. Furthermore it was noted that;
“Diversification benefits of investing in emerging markets are reduced when transactions
costs and, in particular, short-sale constraints are introduced” – (Bekaert, Harvey 2003)
Both the long-term risks and rewards of investing in EMs are strongly linked to the
ability of these markets to develop economically. Empirical analysis of EM investments is
hindered by both the short history and the selection bias of the data as has been described
earlier. Furthermore, major economic, social, and political changes in EMs limit the
applicability of historical data. In their analysis in 2004, Tokat and Wikas sought to blend
theoretical and empirical approaches in determining an investor’s efficient allocation of
wealth including EM indexes to an internationally diversified portfolio. Pursuant to previous
research they point to the fact that over the long run EMs have been shown to enhance
portfolios’ risk adjusted returns. In some shorter periods, however, the empirical case has
broken down. Three short term phenomena that raise the most troubling questions are the
cycle of bull and bear markets, financial crises, and stock market booms and bubbles.
Investors might find it hard to realize the opportunities that EMs present over the long
term. This is due to the fact that EMs often experience significant negative short term
31
deviations away from their long-run averages. Tokat and Wicas highlight this point by
referring to the fact that when the US was in a bear market US investors’ benefit from their
EM exposure was on average, less than the benefit from their exposure to other DMs. In
their findings it was seen that from 1985 – 2003 portfolios that included EMs provided
higher returns and diversification benefits than purely developed market portfolios.
Nevertheless, there have been significant short-term deviations away from this long-term
performance. This point is conveyed in their finding as they show that from 1998 – 2000, for
example, even a modest 3% allocation to EM equities reduced a portfolios return and
increased its volatility despite the presence of imperfect correlation.
It was found that when the US was taken as the relevant developed market, the long term
benefits of EM investment was obscured when there were bull and bear markets. In their
findings the evidence suggests that the performances of equity markets in large economies
have a significant impact on the performances of equity markets in smaller economies. The
results showed that more than 70% of developed international stock markets experienced
bear markets when the US was in a bear market. A smaller amount, 30% of EMs
experienced bear markets in tandem with declines in the US; however this is still significant
in that it reduces the benefits gained from international diversification using EMs.
Furthermore it was found that during bear markets such as after the September 11th attack
on the World Trade Centre, the correlations between the US and emerging markets rose,
precisely at the time when the benefits from diversification were needed the most. This was
conveyed in their statistical findings where they showed that in the most recent US bear
market the correlation between the returns of U.S. stocks and those of EMs increased to
32
70%. It should also be noted however that during bull markets, EMs were found to
outperform the US market which backs up prior research about the high volatility of EMs.
Furthermore, during the EM financial crises such as the Mexican Peso crisis (1994) and the
Asian Currency Crisis (1997) a contagion effect was found. This means that during financial
crises correlations between EMs were seen to spike. US investors’ EM exposure during these
periods reduced portfolio return and increased portfolio volatility. Results showed that
more than 90% of EMs experienced bear markets during the Latin American crisis of 1994 –
1995 and the Asian crisis of 1996 – 1998. These increased correlations would clearly have
negative implications for an investor looking to diversify using EMs.
The final transitory factor included in this research was the effect of stock market’s bubbles
and booms on returns and volatility between EMs and DMs. Investor optimism regarding
the impact of new innovations and profitability in the global economy resulted in a bull
market in the 1990s. Analogous to the previous transitory factors, these boom periods
resulted in correlations between EMs and DMs increasing. Following this the bust periods
then correlations started decreasing again.
However there are still gains to be made from investing in EMs. Financial theory suggests
that higher returns should compensate for the higher volatility of emerging equity markets.
Emerging markets are expected to enjoy faster economic growth than developed markets.
Faster economic growth should translate into faster growth in corporate earnings and thus,
into higher equity market returns. Essentially the long term case for investing in EMs rests
on the idea of enhancing a portfolios return while reducing its risk level through
diversification.
33
“These shorter-term departures from long-term expectations don’t invalidate the long-term
case for investing in emerging markets for risk-tolerant investor” – (Tokat, Wicas 2004)
The application of efficient market theory and historical mean variance analysis
recommends a substantial portfolio allocation to EM equities. This article however
recommends more behavioural and practical considerations which imply that a smaller
allocation to EMs would be more beneficial to the investor. Tokat and Wicas conclude their
article by conveying that investors should consider long term and short term information as
well the fundamental portfolio construction factors in order to determine their own
preferential wealth allocation.
One of the most recent studies on diversification in EMs was performed by
Abumustafa (2007). In his paper he test whether diversification benefits for the investor can
be gained from investing in EMs in the Arab Stock Markets. His paper examines the
relationship between stock prices and economic activity and how this relationship is
relevant to diversification. In their investigation they studied data from the IFC and the
Standard & Poor’s database from 1986 – 2002 6 Arab countries and 3 DMs. In order to
assess the relationship between stock prices and economic activity, Abumustafa used a time
series analysis to see whether increases in the stock market of a country Granger causes
increases in GDP. The results showed that increases in stock prices did indeed cause
increases in GDP. This was also conveyed as having a positive influence for the international
portfolio of the investor looking to allocate wealth to the Arab stock markets.
“We show that the higher the causality between stock market capital and GDP in any
economy, the lower the risk for investors in stock markets” – (Abumustafa – 2007)
34
EM investment management may require extensive, and expensive, on-site company
research, annual fund management expenses among other costs. These can make investors
reluctant to us EMs in their portfolios. A good way for an individual to efficiently invest in
EMs and avoid some unnecessary costs and risks is through a mutual fund. EM funds
concentrate on investments in these markets around the world or in a specific country or
region. Mutual funds offer the advantage of diversification and professional management of
the investors’ wealth.
35
Chapter 3
Research Methodology
36
3.1 – Hypothesis to be Tested
As has been mentioned the aim of this research paper is to analyse EM diversification.
Furthermore to convey whether there is still benefits to be gained for an Irish investor from
holding an international portfolio which consists of both Developed Markets (DM) and
Emerging Markets (EMs), as opposed to a portfolio consisting of only DM indexes. The
investor benefits from investing in both market types through diversification. That is to say
that low correlations exist between EMs and DMs, thereby reducing the risk of the
investor’s portfolio that has a position in both.
The majority if the previous work done on the topic of EM diversification has taken place
before the global financial crisis which started in 2007 therefore this paper includes results
from the years of the credit crunch and investigates the effect that it might have had on
diversification. I will examine whether this particular financial crisis had an impact on the
correlations of EMs and DMs. As well as this, with the ever increasing harmonization of
capital markets and globalization in general, the correlations between markets could very
well be changing. This paper will investigate as to whether there are still diversification
benefits to be gained from investing in EMs and if so how does it compare with the earlier
periods. The primary focus will be from the perspective of the Irish investor. However
correlations for the US with EMs will also be looked at in some detail to make the results
more viable and for comparison.
In order to test the hypothesis this paper will convey the risk return relationship between
EMs and DMs. Secondly I will examine the correlations between DMs and EMs, primarily
from the perspective of an Irish / US investor. Finally I will compare the returns and risks of
portfolios consisting of purely DMs, with portfolios consisting of both EMs and DMs.
37
3.2 - Data Description
Monthly Stock prices indexes for 33 countries from a number of regions around the globe
including Eastern and Western Europe, Asia, North and South America and Africa were
taken from Bloomberg for this research paper. Multiple regions were included to give a
truly global portfolio. It was decided that monthly data should be used as it gives a more
detailed and accurate portrayal as to the behaviour of the given stock market than you
would get from quarterly or yearly data. The time horizon that is included in the data ranges
from the 1st January 1995 to 1st January 2011. The length of the period of 15 years and the
number of test countries chosen are relatively large in comparison to test periods in
previous research in order to minimize the risk of omitted variable bias. The majority of
previous papers as seen in the literature section of the paper include 5 – 10 years of data for
their tests, and for the most part about 10 – 20 countries had only previously been
examined at a time.
As well as investigating the results from 1995 – 2010 there were 3 sub-periods that were
examined also. The sub-period was from January 1995 to September 1999, just before the
introduction of the Euro currency. The penultimate sub-period examined included the
market index prices up until the financial crisis, covering the time horizon from January 2000
to December 2006. The final sub period covered mainly the period of the global recession
from January 2007 to December 2010. The EM and DM relationship and portfolio risk and
returns will also therefore be checked across these different time horizons.
38
Developed Market Indices
ISEQ
Irish Stock Exchange
FTSE 100
Financial Times Stock Exchange
S&P 100
Standard & Poor's
DJ Ind. 30
Dow Jones Industrial Average
CAC 40
Paris Bourse Index
DAX 30
German Stock Index
NIKKEI 225 Japanese Stock Exchange
HSI
Hang Seng Index
SGX
Singaporean Stock Exchange
ASX
Australian Stock Exchange
SMI
Swiss Market Index
Emerging Market Indexes
IBOV
Brazilian Stock Exchange
MICEX
Russian Stock Exchange
SENSEX 30 Indian Stock Exchange
SHCOMP
Shanghai Composite Index
WIG
Warsaw Index
PX 50
Prague Stock Exchange
BUX
Budapest Stock Exchange
SAX
Slovakian Stock Exchange
MERVAL
Buenos Aires Index
IPSA
Santiago Stock Exchange
JCI
Jakarta Stock Index
PSE 30
Philippines Stock Exchange
BURSA
Malaysian Stock Exchange
SET
Stock Exchange of Thailand
TWSE 50
Taiwan Stock Exchange
MEXBOL
Mexican Stock Exchange
39
SASEIDX
Saudi Arabian Stock Exchange
XU 100
Turkish Stock Exchange
MADX
Moroccan Stock Exchange
TUSISE
Tunisian Stock Exchange
KSE
Kuwait Stock Exchange
JALSH
Johannesburg All Share Index
As is highlighted by the above tables stock market index prices were taken from 11 DM
countries and from 22 EM countries. The market index prices from the above countries
were then used to determine monthly returns for each index.
For the first part of my research I wanted to examine and the risk return relationship
between EMs and DMs and compare them across the different sub-periods that were
outlined previously. To do this I used Microsoft Excel to determine the mean returns and
standard deviations of all 33 countries over the 4 different test horizons. The second part of
my research is based around looking at the correlations between Irish Stock Exchange (ISEQ)
and other DMs. Then I will look at the correlations between the ISEQ and EM indexes and
compare the two results over the whole sample horizon as well as across the different sub-
periods. Analogous calculations were also done from a US perspective. Finally in order to
convey the benefits that arise from diversifying a portfolio using EM, I generated a bordered
covariance matrix from the mean returns, standard deviations and correlations found
previously. There will be two portfolios generated for each time period. The first portfolio
will have 50% of funds invested in the ISEQ as the home market and the remaining 50%
equally weighted amongst the rest of the DMs. The second portfolio will consist of 50%
invested in the ISEQ and the remaining wealth equally weighted amongst both the EMs and
the DMs.
40
3.3 – Relevant Formulae
After getting the stock market index prices for the 33 countries from Bloomberg I was then
able to determine the monthly returns for each monthly observation of the indexes for each
time period. This and the rest of the calculations were done through Microsoft Excel.
Monthly returns were determined as follows:
Monthly Returns:
Where:
Rit = the monthly return of index (i) at time (t).
Pit = the value of the stock index (i) at time (t).
Pit-1 = the value of stock index (i) at the previous time period (t-1).
As has been outlined it is generally considered that mean returns and standard deviations
are acceptable proxies for clarifying levels of risk and return. For the first part of my
research I identify the degree of risk and returns associated with each of the 11 DMs and 22
EMs and compare the relationship between the two in each time period and across the
different sub-periods. The calculations for risk and return were done using these formulae
for mean monthly returns, variance and standard deviation:
41
Mean Monthly Returns:
∑
Where:
= Mean return of the monthly returns for the stock index (i).
n = Number of monthly observations.
Variance & Standard Deviation:
∑
σi = √
Where:
σi2 = the variance of monthly returns of stock index (i)
Rit = the value of the return of stock index (i) at time (t)
= the mean monthly return of stock index (i)
Pursuant to previous research I thought it would be beneficial to calculate the absolute
growth of each of the DMs and EMs per period. This was done to highlight the differences
between the two and to convey the relatively high growth opportunities that diversifying
into EMs can have for the investor. This simple formula for periodic growth was used:
42
Periodic Growth:
Where:
GiT = the absolute growth of index (i) for the time horizon (T).
PiEND = the market index price at the end of the sample horizon.
PiBEG = the market index price at the beginning of the sample horizon.
Prior to calculating the covariance matrix the correlation matrix between indexes was
generated using the correlation function on Microsoft Excel. Now that the inputs of
standard deviations and correlations have been found this leads to the next step which is
calculating the co-variance matrix:
Co-Variance:
Where:
Covij = the covariance between index (i) and index (j).
σi = the mean standard deviation of index (i) for that time period.
σj = the mean standard deviation of index (j) for that time period.
= the correlation co-efficient between index (i) and index (j)
Between the inputs that have been calculated using the above formulae and the correlation
and covariance matrices generated using Excel there are now sufficient inputs to determine
the portfolio returns and standard deviations. The portfolio return was found simply by
getting the weighted average of the index mean returns. The portfolio variance was found
using the below formula through generating a bordered covariance matrix based on the
43
weights invested the relevant indexes and the co-variances calculated from the above
formula. The portfolio standard deviation is simply the square root of the portfolio variance.
Portfolio Return:
∑
Where:
RP = the return on the portfolio.
Wi = the weight of the portfolio invested in index (i).
= the mean monthly return of stock index (i).
Portfolio Variance:
∑∑
Where:
σP2 = the variance of the portfolio.
Wi = the weight invested in index (i).
Wj = the weight invested in index (j).
Cov (ri, rj) = the covariance of returns between index (i) and index (j).
44
Chapter 4
Data Analysis
45
My data analysis will be split up into three different sections. The first section will outline
the risk, return and growth characteristics of Emerging Markets (EMs) and Developed
Markets (DMs). As has been stated the mean monthly returns I have calculated will
represent the returns, and standard deviations will be used as the measure of risk for the
indexes. This part of the analysis was important in identifying the trends and characteristics
that might be present between EMs and DMs, as well as between the different sample
horizons. In the penultimate section I will portray the diversification opportunities
presented by EMs by looking at correlations between EMs and DMs from the perspective of
an Irish investor and from a US investor. The perspective of the US investor was also taken
to for comparison purposes with the S&P 500 as the benchmark DM. In my final section I
will look to compare an Irish denominated portfolio that is solely invested in DM securities,
with an Irish portfolio that is equally invested in EMs and DMs. The data analysis in each
section will be split into an analysis of the four different time periods from 1995 – 2010,
1995 – 1999, 2000 – 2006 and from 2007 – 2010.
4.1 – Risk, Return and Periodic Growth
In order to get a true understanding of the potential benefits from diversifying using EM
securities it was vital to look at the market risks and returns that would be associated with
the different DM and EMs indexes. In the data I sought to identify trends and characteristics
between EMs and DMs. Periodic growth is also used to further convey the potential
opportunities that can be harnessed by investing in EM securities.
46
1995 - 2010
The first time horizon that I looked for risk and return was from January 1995 – December
2010. It could be expected that returns and risk over this time period covering 15 years
should have relatively less extreme results for risk and return than the sub-periods due to
the fact that it is generally accepted by economists that index returns generally possess
mean reverting tendencies. Table 1.1 shows the monthly returns and risk in the form of
standard deviation for DMs and EMs in the first test period. The results were calculated
from the market price indexes outlined in the previous chapter.
Table 1.1: Index Risk and Returns 1995 - 2010
Developed Markets
Emerging Markets
Return Std.Dev
Return Std.Dev
UK 0.004432 0.041774
BRAZIL 0.020682 0.093155
US(S&P) 0.006088 0.046283
RUSSIA 0.02811 0.134543 US(DJ) 0.00664 0.045494
INDIA 0.012474 0.077666
FRA 0.005641 0.056761
CHINA 0.012488 0.088705 GER 0.008524 0.066187
POLAND 0.014104 0.085656
JAP -0.00092 0.058875
CZECH 0.007662 0.072801 IRE 0.004075 0.059954
HUNG 0.019159 0.088301
HK 0.00825 0.075829
SLOVAK 0.009457 0.060262 SING 0.006597 0.048484
ARG 0.018538 0.108887
AUS 0.00469 0.041654
CHILE 0.010355 0.05446 SWISS 0.005965 0.048261
INDO 0.015044 0.087308
PHILLI 0.013649 0.077403
MALAY 0.006006 0.069955
THAI 0.003241 0.094063
TAIWAN 0.0047 0.077981
MEXICO 0.019759 0.072567
SAUDI 0.011611 0.071319
TURKEY 0.039111 0.149926
MORROC 0.008502 0.058706
TUNISIA 0.008752 0.032731
KUWAIT 0.014937 0.082605
SAFRICA 0.011724 0.059301
47
In order to get an idea of aggregate differences between EMs and DMs pertaining to risk
and returns Table 1.2 shows the average returns and standard deviations for DMs and EMs
in the first time horizon that was examined. From the data below we can see that EMs
display higher returns by 0.008641, and higher risk level for the investor by 2.81% over the
15 year horizon. The maximum return found among the EM indexes was for Turkey at
0.0319. This is significantly higher than the highest return found among DMs which was the
German DAX at 0.0085. Similarly results were found in relation to standard deviation where
Turkey, the EM country with the highest risk, had a standard deviation of 14.99%. This is
relatively very high in comparison to Hong Kong which was the riskiest DM index with a
7.58% standard deviation.
Table 1.2: Average DM, EM Risk, Returns 1995 - 2010
DMs EMs
Return 0.005453 0.014094 Std.Dev 0.053596 0.081741
Data for periodic growth was also calculated for each of the 11 DMs and 22 EMs. The results
for which are shown in Appendix 1. Table 1.3 here shows the average, maximum and
minimum growth for EMs and DMs for the first time horizon from 1995 – 2010. From the
table below one can clearly see that growth opportunities for investment in EMs are
considerably larger on average than those available in DMs. Over the 15 year period DM
indexes saw an average growth of 127%. EMs on the other hand experienced growth, on
average of 1,720%. That’s an average growth rate of over 17 times the original market price
from the beginning of 1995 to the end of 2010 for EMs.
48
Table 1.3: Average, Max. and Min. Growth levels 1995 - 2000
DMs EMs
Average 1.279926 17.20943 Max 2.295846 225.6404 Min -0.40018 -0.19846
1995 – 1999
The risk and returns and the periodic growth for the 33 countries were then calculated for
the 5 year period from January 1995 to December 1999. The returns and standard deviation
for this first sub-period can be seen in Table 1.4. During the analysis of these results it was
noted that Mexico was emerging from the Peso crisis of 1994 and also during this time
horizon the Asian Currency Crisis which began in Thailand in 1997 had occurred.
49
Table 1.4: Index Risk and Returns 1995 - 1999
Developed Markets
Emerging Markets
Mean Std.Dev
Mean Std.Dev
UK 0.013318 0.034944
BRAZIL 0.029934 0.118341 US(S&P) 0.0186 0.041228
RUSSIA 0.043339 0.192604
US(DJ) 0.018334 0.044141
INDIA 0.009114 0.079734 FRA 0.018976 0.056303
CHINA 0.023782 0.098782
GER 0.018294 0.060559
POLAND 0.020671 0.115141 JAP 0.002332 0.059716
CZECH 0.003569 0.073345
IRE 0.017824 0.045465
HUNG 0.038523 0.116082
HK 0.01222 0.09537
SLOVAK 0.019213 0.048857 SING 0.021953 0.04368
ARG 0.015681 0.111316
AUS 0.012291 0.033248
CHILE 0.003689 0.070158 SWISS 0.019613 0.057723
INDO 0.009952 0.112705
PHILLI 0.029758 0.073166
MALAY 0.020898 0.126218
THAI -0.01389 0.127145
TAIWAN 0.005904 0.079524
MEXICO 0.026032 0.090757
SAUDI 0.007111 0.045311
TURKEY 0.06944 0.167993
MORROC 0.022419 0.059905
TUNISIA 0.003413 0.038253
KUWAIT 0.012852 0.106511
SAFRICA 0.010906 0.070009
The average return and standard deviation for the second test period can be seen in Table
1.5. As was the case in the 15 year time horizon I examined the EM indexes and they
showed higher levels of both return and risk than the DM indexes. From the results we can
see that growth is also noticeably higher in EMs than in DMs thou to a far smaller extent
than in the first test period. This is largely likely to be because this test period is 10 years
shorter in its horizon than the first giving far less scope for growth. It should also be noted
that the difference between EMs and DMs in standard deviation is considerably larger in
this second time period than in the first this is likely due to swings in returns that would
have been caused by the Asian Currency Crisis. This information is re-enforced by the fact
that the lowest mean monthly returns among the EM indexes from 1995 – 1999 were seen
50
in the South East Asian countries. Furthermore Thailand actually experienced the lowest
mean monthly return of all the indexes at -0.014 for the period. The data in Table 1.4 could
also be said to portray the contagion effect that the Asian currency crisis had. That is to say
that Russia went through its own financial crisis as a direct result of the Asian currency crisis.
This is conveyed by the fact that as seen in Table 1.4 Russia experienced the highest risk
level from 1995 – 1999 with a standard deviation of 19.26%.
Table 1.5: Average DM, EM Risk, Returns and Growth 1995 – 1999
DMs EMs
Mean 0.015796 0.018742 Std.Dev 0.052034 0.096448 Growth 1.273372 2.078162
2000 – 2006
The penultimate sub-period under which the risk, return and absolute growth are to be
examined is from January 2000 to September 2006. This sub-period spans a horizon from
the “Dot Com” bubble up until just before the beginning of the global financial crisis which
began in 2007. Table 1.6 depicts the monthly returns and standard deviations for the third
test period which was calculated from the monthly stock index prices from Bloomberg.
51
Table 1.6: Index Risk and Returns 2000 - 2006
Developed Markets
Emerging Markets
Mean Std.Dev
Mean Std.Dev
UK 0.000631 0.037858
BRAZIL 0.015362 0.081237 US(S&P) 0.001046 0.04109
RUSSIA 0.020834 0.096092
US(DJ) 0.002416 0.041179
INDIA 0.014215 0.069473 FRA 0.001155 0.052774
CHINA 0.008802 0.066152
GER 0.002019 0.069184
POLAND 0.013627 0.064985 JAP -8.3E-05 0.053597
CZECH 0.014726 0.062837
IRE 0.009186 0.050883
HUNG 0.013664 0.064752
HK 0.004555 0.055235
SLOVAK 0.003345 0.062934 SING -0.00184 0.04522
ARG 0.022412 0.119562
AUS 0.001703 0.038456
CHILE 0.011277 0.044769 SWISS 0.003794 0.041521
INDO 0.014959 0.06856
PHILLI 0.00232 0.081978
MALAY 0.001157 0.06782
THAI 0.00711 0.075627
TAIWAN 0.000253 0.07709
MEXICO 0.018778 0.061757
SAUDI 0.019555 0.073582
TURKEY 0.019171 0.136286
MORROC 0.006947 0.051264
TUNISIA 0.017748 0.037462
KUWAIT 0.020814 0.084561
SAFRICA 0.017524 0.053284
Table 1.7 below summarises the data presented previously by conveying the average risk,
return and periodic growth that was displayed by the 11 DM indexes and the 22 EM indexes.
Pursuant to the previous two periods examined it can be seen from this that EM indexes
again displayed higher volatility and return levels than the DM indexes. Average monthly
return among the EM indexes was 1.07% higher than in the DM indexes. Also in tandem
with previous calculations, the average monthly standard deviation of EMs was 2.49%
higher than in DMs. Furthermore, the below table depicts considerably higher growth for
the EM indexes of over 165%, in comparison to only an average 12% growth for DM indexes.
52
Table 1.7: Average DM, EM Risk, Returns and Growth 2000 - 2006
DMs EMs
Mean 0.002235 0.012936 Std.Dev 0.047909 0.072821 Growth 0.120341 1.654209
2007 – 2010
The final time sub-period covers the horizon from the beginning of the global recession in
2007 up until the end of 2010. As in the previous sub periods standard deviations and mean
monthly returns were calculated as indicators for risk and return. Table 1.8 shows the
results generated from the Bloomberg market price indexes for the final sub-period.
Table 1.8: Index Risk and Returns 2007 - 2010
Developed Markets
Emerging Markets
Mean Std.Dev
Mean Std.Dev UK 0.000279 0.051968
BRAZIL 0.012211 0.074336
US(S&P) -0.00119 0.057498
RUSSIA 0.024311 0.049543 US(DJ) -0.00043 0.052941
INDIA 0.012153 0.091743
FRA -0.00644 0.059851
CHINA 0.0065 0.11116 GER 0.00243 0.064064
POLAND 0.000133 0.078421
JAP -0.00876 0.069186
CZECH -0.00249 0.088168 IRE -0.02104 0.081246
HUNG 0.001071 0.084287
HK 0.006178 0.08097
SLOVAK 0.000931 0.062432 SING 0.001473 0.054861
ARG 0.015962 0.09277
AUS 0.000383 0.052872
CHILE 0.012597 0.050374 SWISS -0.00646 0.043942
INDO 0.019781 0.085065
PHILLI 0.006078 0.061514
THAI 0.013026 0.078459
TAIWAN 0.006357 0.078959
MEXICO 0.009172 0.063504
SAUDI 0.002654 0.089405
TURKEY 0.014909 0.098446
MORROC -0.00779 0.066871
SAFRICA 0.004817 0.061001
53
The next piece of data as seen in Table 1.9 summarises the data gathered on risk, return and
growth for the horizon that covers the epoch of financial turmoil. The results from this
period are considerably different within each market type. Six out of the 11 DM indexes
actually experienced negative mean monthly returns in tandem with the recession. In the
periods from 1995 – 2010, 1995 – 1999 and 2000 – 2006 there were only 1, 0 and 2 indexes
respectively that displayed negative mean returns. From the data we can see that average
returns in EMs were again higher than in DMs. The difference in average monthly returns in
this case is 1.1% which is the highest out of the 4 test periods which should be noted. As
well as this the recession period EMs displayed higher risk than in DMs. Standard deviation
in EMs was 7.71% and in DMs the figure was 6.09%. The difference of 1.63% between the
two is the smallest difference amongst the time periods.
Table 1.9: Average DM, EM Risk, Returns and Growth 2007 – 2010
DMs EMs
Mean -0.00305 0.00802 Std.Dev 0.060854 0.077182 Growth -0.16878 0.661133
54
4.2 – Correlations
In this section of my data analysis I will examine the relationship between EM and DM
indexes using correlation coefficients. As was described in detail in my literature review and
research methodology the correlation co-efficient measures the degree to which indexes
move in tandem with one another. The scale on which this is measured is from -1 to +1.
Where -1 signifies perfect negative correlation, 0 implies that the index would be
uncorrelated and a reading of +1 between indexes means that the indexes in question are
perfectly correlated. That is to say that, under perfect positive correlation if the return on
index A increases 10%, the return on index B also increases 10%. In the case of perfect
negative correlation; if index A increases 10%, this would lead to a decrease in returns for
index B of 10%.
Relating to this study, in order to benefit from diversification the investor should seek to
invest in indexes that have imperfect or even negative correlations where possible in order
to efficiently minimize risk. In my data I will be primarily looking at correlations from the
perspective of the Irish investor. However correlations from the point of view of a US
investor were also looked at for comparison and as a benchmark. Other significant
correlations will also be noted. This researcher was also looking to see if there were any
considerable trends or variation in the strength of correlations across the whole horizon and
between the three sub-periods that were examined. As has been outlined this is done to
check whether the increasing global market integration has affected correlation levels.
Furthermore particular attention is paid to correlations in the most extreme period of the
financial crisis from 2007 – 2010. As in the previous section of this chapter my analysis of
the data will be split into looking at the whole sample horizon from 1995 – 2010, and into
55
the 3 sub-periods from 1995 – 1999, 2000 – 2006 and from 2007 – 2010. Correlations were
determined based on the monthly index returns that were calculated from the different
index market prices on Bloomberg. The correlation matrices among the 11 DM indexes and
22 EM indexes can be seen from Figure 1 – Figure 4 which follows.
56
57
58
59
60
1995 – 2010
The correlation matrix as seen in Figure 1 shows correlations amongst the 11 DM indexes
and the 22 EM indexes for the overall sample horizon from 1995 – 2010. To convey the
benefits of investing in EM indexes from an Irish perspective I looked at the ISEQ’s average
correlation with other DM indexes and compared it with the ISEQ’s average correlation with
EM indexes. This can be seen in Table 1.10.
Table 1.10 – ISEQ Correlations 1995 - 2010
ISEQ Correlations DMs ISEQ Correlations EMs UK 0.706784
BRAZIL 0.505768
US(S&P) 0.7098
RUSSIA 0.014966 US(DJ) 0.658746
INDIA 0.386687
FRA 0.678551
CHINA 0.096638 GER 0.656807
POLAND 0.442365
JAP 0.486589
CZECH 0.441297 HK 0.429665
HUNG 0.525904
SING 0.647879
SLOVAK 0.62888 AUS 0.732701
ARG 0.313657
SWISS 0.630847
CHILE 0.408444
INDO 0.375253
Average 0.633837
PHILLI 0.578755 Max 0.732701
MALAY 6.92E-05
Min 0.429665
THAI 0.351702
TAIWAN 0.414776
MEXICO 0.50731
SAUDI 0.299064
TURKEY 0.378967
MORROC 0.665278
TUNISIA 0.044277
KUWAIT -0.03988
SAFRICA -0.09715
Average 0.329228
Max 0.665278
Min -0.09715
From this data we can see as expected that the ISEQ moves most closely in tandem with
other developed European countries and the US. The ISEQ has a correlation of 0.7068 with
61
the FTSE in the UK. The ISEQ’s correlation with the US S&P100 is also very high at 0.7098.
This makes sense as the UK and the US have been two of Ireland’s biggest trading partners
over the last couple of decades. Therefore these DM indexes could be said to provide very
little opportunity for diversification for an Irish investor based on these high positive
correlations. Table 1.10 also gives us the correlations between the ISEQ and the 22 EM
indexes over the 15 year sample horizon. One can clearly see from these results that the
ISEQ is less correlated with the EM indexes than with the DM indexes. In two of the
instances the ISEQ has actually displayed negative correlation with other indexes.
Correlations of -0.0399 and -0.0972 can be seen with Kuwait (KSE) and South Africa (JALSH)
respectively for the ISEQ. As well as this Table 1.10 shows us the average correlation
between DMs, EMs and the ISEQ to give us an overall perspective. Ireland (ISEQ) had an
average correlation of 0.6338 with the 11 DMs. The average correlation with EMs was
considerably lower at 0.3292. These lower correlations assist to convey the diversification
benefits that EM indexes have provided for the Irish investor over the last 15 years. In order
to ensure that this was not an isolated occurrence for Ireland as a DM country similar
correlation results were also found from the perspective of a US investor as seen in
Appendix 5.
1995 – 1999
The correlations of monthly returns between the 33 indices fort the first sub-period is
shown in Figure 2. From the correlations seen in Table 1.11 we can again see that EM
indexes provide superior diversification opportunities to the Irish investor. In the overall
time period of 15 years we previously witnessed that 2 EM indexes displayed negative
correlations with the ISEQ. In this sub-period from 1995 – 1999 Russia, China, Malaysia,
62
Saudi Arabia, Kuwait and South Africa all displayed negative correlations with the ISEQ.
South Africa again showed the lowest correlation with the ISEQ at -0.1844. Consistent
with the results over the entire sample horizon, this sub-period exhibited lower correlations
between the ISEQ and EM indexes, than between the ISEQ and DM indexes. The average
correlation between ISEQ and DMs was seen to be 0.6136. A considerably lower average
correlation of 0.2881 can be seen between Ireland and the EM indexes. In comparison to
results from the 15 year sample horizon Ireland’s correlations were down in both DMs and
EMs for this sub-period. Furthermore US correlations exhibit very similar characteristics in
their relationship with the other indexes.
63
Table 1.11 – ISEQ Correlations 1995 - 1999
ISEQ Correlations DMs ISEQ Correlations EMs UK 0.769703
BRAZIL 0.536496
US 0.706717
RUSSIA -0.03402 US 0.638838
INDIA 0.222218
FRA 0.572095
CHINA -0.0961 GER 0.629112
POLAND 0.438011
JAP 0.328717
CZECH 0.227174 HK 0.381947
HUNG 0.612961
SING 0.674739
SLOVAK 0.599686 AUS 0.762166
ARG 0.428979
SWISS 0.671714
CHILE 0.524344
INDO 0.427222
Average 0.613575
PHILLI 0.53847 Max 0.769703
MALAY -0.00339
Min 0.328717
THAI 0.367269
TAIWAN 0.323301
MEXICO 0.469729
SAUDI -0.05992
TURKEY 0.29515
MORROC 0.68752
TUNISIA 0.072503
KUWAIT -0.05483
SAFRICA -0.18441
Average 0.288107
Max 0.68752
Min -0.18441
2000 - 2006
Figure 3 shows the correlation matrix for monthly returns of the chosen markets for the
second sub-period from 2000 – 2006. In tandem with the previous two test horizons the
second sub-period examined here shows improved diversification benefits for the Irish
investor who invests in EM indexes. Negative correlations can be seen between the ISEQ
and 3 EMs. As in the previous horizons the ISEQ displays only positive correlations with
DMs. This sub-period displays the highest inter DM correlations so far with the average
correlation of the 11 DM indexes with the ISEQ at 0.6692. The average correlation between
64
Ireland and EM indices is 0.2932 which is higher than the equivalent result from the sub-
period prior but still more than result from the overall 15 year sample horizon. However
EMs still display superior correlations for diversification purposes to DMs by 0.376. Very
similar results are again seen for the benchmark US S&P 100 index in Appendix 7.
Table 1.12 – ISEQ Correlations 2000 - 2006
ISEQ Correlations DMs ISEQ Correlations EMs
UK 0.701121
BRAZIL 0.514543 US 0.737412
RUSSIA 0.050302
US 0.702038
INDIA 0.355955 FRA 0.715351
CHINA 0.112199
GER 0.746343
POLAND 0.374691 JAP 0.475521
CZECH 0.350027
HK 0.547332
HUNG 0.431548 SING 0.649292
SLOVAK 0.684391
AUS 0.729554
ARG 0.11934 SWISS 0.68768
CHILE 0.40589
INDO 0.263657
Average 0.669164
PHILLI 0.634164 Max 0.746343
MALAY -0.27085
Min 0.475521
THAI 0.318603
TAIWAN 0.374329
MEXICO 0.581353
SAUDI 0.103829
TURKEY 0.439404
MORROC 0.704667
TUNISIA 0.009422
KUWAIT -0.07119
SAFRICA -0.03528
Average 0.293227
Max 0.704667
Min -0.27085
2007 – 2010
The correlation matrix between the 33 market index monthly returns for the period
spanning the height of the financial crisis from 2007 – 2010 can be seen in Figure 4. The
correlations between the ISEQ and the 32 other DM and EM indexes is summarized below in
65
Table 1.13. As has been the case in all test periods with this study it is again seen that
Ireland has lower correlations with the EM indexes than with the rest of the DM indexes. It
should be noted that in comparison to other periods the highest levels of correlations can
be seen between the ISEQ and each of DM and EM indexes. The average correlation
between ISEQ and DM indexes for this sub-period was 0.6729. The sub-period that exhibited
the next highest average correlation between the ISEQ and DM indexes was from 2000 –
2006 which was had an average correlation of 0.6692. Furthermore the average correlation
between the ISEQ and EM indexes was relatively even larger again in comparison with other
periods. A correlation of 0.5186 was seen between the ISEQ and EM indexes for the sub-
period data displayed in Table 1.13. This is a very considerable increase in correlation of
0.2254 from the previous period. The control results from the perspective of the US investor
also showed very similar results. The US S&P 100 also showed inter sub-period highs for
correlation of 0.8273 and 0.6072 with DM and EM indexes respectively.
66
Table 1.13 – ISEQ Correlations 2007 – 2010
ISEQ Correlations with DMs ISEQ Correlations with EMs
UK 0.728832
BRAZIL 0.615829 US 0.732402
RUSSIA 0.145551
US 0.670384
INDIA 0.549753 FRA 0.758113
CHINA 0.198525
GER 0.692799
POLAND 0.620984 JAP 0.607002
CZECH 0.686448
HK 0.446694
HUNG 0.616704 SING 0.700808
SLOVAK 0.665716
AUS 0.759542
ARG 0.600687 SWISS 0.632396
CHILE 0.472974
INDO 0.584406
Average 0.672897
PHILLI 0.706928 Max 0.759542
THAI 0.591567
Min 0.446694
TAIWAN 0.643882
MEXICO 0.569451
SAUDI 0.579666
TURKEY 0.529913
MORROC 0.61469
SAFRICA -0.13972
Average 0.518629
Max 0.706928
Min -0.13972
67
4.3 – Irish Denominated Portfolios
As was conveyed by previous research outlined in my literature review, it is generally
accepted that there are diversification benefits to be gained by investing a proportion of a
portfolio in EMs as well as in DMs. In this section I will be looking at portfolio returns and
risk from the perspective of an Irish investor. In total for this investigation I will be looking at
two portfolios per time period. As was the case in the previous section the time periods
under examination are from 1995 – 2010, 1995 – 1999, 2000 – 2006, 2007 – 2010. In each
test period I will calculate risk and returns for one portfolio purely invested in DM indexes.
The second portfolio in each test period will consist of wealth invested in both DM and EM
indexes. For each portfolio 50% of wealth will be taken to have been invested in the ISEQ as
the domestic market. This is due to the theory of imperfect international diversification
(home bias) that was discussed in the literature review chapter of my research. The
remainder of wealth invested was equally weighted among DM indexes one portfolio per
time period. For the second portfolio the remaining 50% was invested equally among DM
and EM indexes.
In order to figure out the portfolio returns I simply took the weighted average of mean
monthly returns for each index within that time period. The formula for which can be seen
in detail in my research methodology. The mean monthly returns were calculated as has
been described from the 33 market index prices taken form Bloomberg.
However in order to find the portfolio risk in the form of standard deviation the process was
somewhat more complex. The first step in this situation was to calculate the co-variance
matrix. The inputs needed for this were the mean monthly returns from each index as well
as the correlation matrix. The formula for co-variance (which is described in more detail in
68
the research methodology and literature review chapter of my research) was then used
through Microsoft Excel to generate the covariance matrix. The covariance matrix for two of
the DM index only portfolios for the test periods from 1995 – 2010 and from 1995 – 1999
can be seen in Figure 5 and Figure 6 respectively. As well as this, Figure 7 displays the co-
variance matrix that I calculated for the portfolio consisting of DM and EM indexes for the
15 year sample horizon from 1995 – 2010.
From here the next stage in determining the portfolio risk was to undergo the bordered
covariance matrix. The inputs that were required for this were the co-variances calculated
from the previous step as well as the relevant weights for the given indexes. As can be seen
from Figure 5 and Figure 6 50% of the portfolio was invested in the ISEQ. The remaining 50%
(0.5) was equally weighted among the 10 remaining DM indexes giving them a weight of 5%
(0.05) each. In the case of the portfolio compiled of DM and EM indexes the residual 50% of
wealth after investment in the ISEQ was split among the remaining 32 indexes which meant
they carried a weight of 1.563% (0.01563) as is shown in Figure 8. Each column in the
bordered covariance matrix is then added up, and the sum of the totals of each column is
the variance of the portfolio. The standard deviation as has been described is the simply the
square root of the variance and then we have the portfolio risk. The rest of the co-variance
and bordered covariance matrices can be seen from Appendix 9 to Appendix 16.
69
Figure 5 - DM Portfolio 1995 – 2010
Figure 6 – DM Portfolio 1995 – 1999
70
Figure 7 – DM+EM Portfolio Covariance Matrix 1995 - 2010
71
Figure 8 – DM+EM Portfolio Bordered Covariance Matrix 1995 – 2010
72
In order to convey the benefits from diversifying an Irish portfolio using EM indexes I
compared purely DM portfolios with portfolios consisting of both EM and DM indexes. This
relationship was looked at over the 15 year time horizon as well as compared between the
three sub-periods. The results from this investigation are summarised in Table 1.14. In the
below table RP1 is the portfolio return and σP1 is portfolio risk or standard deviation for the
portfolio consisting of DM indexes only. RP2 and σP2 represent return and risk for the
portfolio invested in both EM and DM indexes.
Table 1.14 – Portfolio Summary Statistics
Period RP1 RP2
σP1 σP2
1) 1995-2010 0.004833 0.007756
0.048868 0.046821 2) 1995-1999 0.016708 0.017791
0.040421 0.03963
3) 2000-2006 0.005363 0.009281
0.043235 0.039974 4) 2007-2010 -0.01115 -0.00811
0.062883 0.063595
As was expected from previous research examined in my literature review the portfolio
consisting of EM and DM indexes outperformed the portfolio that only consisted of DM
indexes over the 15 year sample horizon. From the above data it can be seen that return for
the DM portfolio (RP1) was outperformed by the DM, EM portfolio (RP2) by
0.00293.Furthermore, and contrary to the fundamental rules of the risk return relationship
discussed earlier, the EM, DM portfolio (σP2)provided a lower level of risk for the Irish
investor by 0.00205.
For the 15 year time horizon and the 3 sub-periods that were examined RP2 was better than
RP1. The effect of the financial crisis can be seen in the portfolio returns from the sub-period
from 2007 – 2010. Even though RP2 showed negative returns it still provided a favourable
alternative to investment in the DM portfolio. For the most part the EM, DM portfolio
73
outperformed the DM portfolio in terms of displaying the lowest level of risk as well. The
period where the EM, DM portfolio outperformed the DM portfolio the most was in the
second sub-period from 2000 – 2006 where σP1 was 0.043235 and risk in σP2 was lower at
0.03997. It should be noted that in the period covering the height of the global recession the
DM portfolio actually presented a lower level of risk in comparison to the portfolio
consisting of EM and DM indexes.
74
Chapter 5
Empirical Findings
75
In this chapter I will link in the findings from my research in the literature review in Chapter
2 with the results I attained from the data analysis in the previous Chapter 4. In my
hypothesis I sought to investigate the contemporary risk return relationship between DM
and EM indexes. Secondly my aim was to examine whether there is still substantially low
correlations to avail of for the Irish investor looking to invest in EMs. In the final section of
my research I looked at the benefits that can be gained from an Irish investor who
diversifies using EM indexes in a portfolio. This was tested against an Irish portfolio
consisting of only DM indexes.
Market index prices were taken as benchmarks to represent investment in different
countries as previous research had shown that a high degree of homogeneity exists within
EMs (Divech, Drach 1992). That is to say that all stocks in the EM are very sensitive to
changes in the underlying stock market index. As has been stated there were 33 countries
included in the data set. This consisted of 11 DM indexes and 22 EM indexes. These
countries were selected from various regions around the world including Eastern Europe,
South/Central America, Africa, East/South-East Asia, Indonesia and the Middle East. The
reason such a large number and a wide spectrum of indexes was in tandem with Lessard
(1976) who showed if markets are segmented then a more complete diversification of
country effects should be available. The large time horizon of 15 years was selected because
of the fact that it was frequently mentioned in previous research that the empirical analysis
of EM investments was hindered by the short term history of the data (Tokat, Wikas 2004).
It was also vital to include the three sub-periods that have been outlined due to the fact that
investors might find it hard to realize the opportunities that EM indexes present over the
76
long term. This is due to the fact that EMs often experience significant negative short term
deviations away from their long run averages (Tokat, Wikas 2004).
For the first stage of my investigation into diversification it was important to look at the risk
return relationship between DM and EM indexes. This was to see whether EMs still offer
higher returns relative to DMs, and if so, by how much. The second distinctive difference
between DMs and EMs is that EMs historically have displayed higher risk levels in
comparison to DMs. Figure 9 depicts this risk return relationship.
In the graph R1 represent s the average monthly return for the 11 DM indexes for the
relevant time period. R2 displays the average return for the 22 EM indexes, while σ1 and σ2
represent risk for DM and EM indexes respectively in the form of average standard
deviations. Again risk is shown for the overall 15 year time horizon from 1995 – 2010 as well
as for the 3 sub-periods.
Figure 9: DM and EM Risk and Returns
Pursuant to previous research by Errunza (1983), my findings summarised by Figure 9 show
that for every time period examined the average risk and return displayed by EM indexes
-0.02
0
0.02
0.04
0.06
0.08
0.1
0.12
1995-2010 1995-1999 2000-2006 2007-2010
R1
R2
σ1
σ2
77
was higher than those calculated by DM indexes. The effect of the global bull market of the
1990’a can also be seen from Figure 9 with average returns from DM indexes were at their
highest. It was noted that the sub-period in which there was the highest difference in
average risk level between DM and EM indexes was from 1995 – 1999. This spike in the
average EM index risk level is likely due to the Asian Crisis that began in 1996 with the
devaluation of the Thai Baht. The Asian Crisis also lead to a financial crisis is Russia which as
pointed out in my data analysis chapter experienced a huge risk level for that period with a
standard deviation of almost 20%. Despite the fact that there was a relative bull market
amongst DMs in this period, and a bear market for EMs in the form of the Asian and Russian
Crises, EMs still managed to display higher average returns against DMs.
From Figure 10 the superior growth opportunities provided by EMs can clearly be seen. The
graph depicts periodic growth of DMs against EMs for the 15 year time horizon and each of
the three sub-periods. It can clearly be seen that EMs provide consistently higher growth
than DMs. The reason that the growth for EMs is far higher in the first period is due to the
length of the time horizon in comparison to the other periods. The 15 year time horizon
gives EM indexes much higher scope for growth than the three sub-periods of 5, 7 and 4
years respectively.
78
Figure 10: DM and EM Periodic Growth
In the penultimate section of my research I looked at whether investing in EM indexes
provided lower correlation levels for an Irish investor than investing in DM indexes. Figure
11 conveys correlations between the ISEQ and DM and the ISEQ and EM indexes. The
average correlation between the ISEQ and the rest of the 10 DM indexes is represented by
p1. The average correlation between the ISEQ and the 22 EM indexes is shown by p2. The
difference between the ISEQ’s correlation and DMs, and it’s correlation with EMs, is
conveyed by p1 – p2. Correlations are shown for the overall 15 year time horizon as well as
for the three sub periods from 1995 – 1999, 2000 – 2006 and for the period of the global
financial crisis from 2007 – 2010. In tandem with prior research (Divech, Drach 1992) I found
that correlations amongst DMs were considerably higher than correlations amongst EMs,
and between DM and EM indexes as is portrayed in Figure 11.
-2
0
2
4
6
8
10
12
14
16
18
20
1995-2010 1995-1999 2000-2006 2007-2010
DM
EM
79
Figure 11: ISEQ Correlations with DM and EM Indexes
There were three main findings from my investigation of correlations between DM and EM
indexes. Firstly, pursuant to previous empirical research I found that correlations amongst
DMs and between DM and EM indexes have increased over time across the three sub-
periods. This is in tandem with finding s made by Bekaert, Harvey (1997) who showed that
EM correlations were increasing over time in tandem with the ever increasing global
integration of capital markets.
The second intrinsic finding I made from my correlation results was that during the period of
the DM bull market in the 1990’s, the ISEQ displayed its lowest correlation with the EM
indexes at 0.2881. It can also be seen from Figure 11 that the inter-DM correlation during
this period was at its lowest relative to the other sub-periods and the 15 year time horizon.
This indicates that this would have been the most favourable sub-period to invest in EMs
based on the low risk associated with low correlations. These findings support previous
research carried out by Tokat, Wikas (2004) where they investigated the effects of bull and
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
1995-2010 1995-1999 2000-2006 2007-2010
p1
p2
p1-p2
80
bear markets on correlations between EMs and DMs. They also found that correlations
between DMs and EMs were at their lowest during bull markets.
There were also important findings from my results from the final sub-period covering the
height of the financial crisis. As can be seen from Figure 11 the period from 2007 – 2010
exhibited the highest correlations between the ISEQ and both DM and EM indexes of 0.6729
and 0.5186 respectively. In order to improve the validity these results correlations were also
looked at from the perspective of the US investor. The results from the US investor’s
perspective mirrored the ISEQ’s correlations, with the S&P 100 also displaying periodic highs
during the financial crisis of 0.8273 and 0.6072 (Appendix 8) with DM and EM indexes
respectively. This is in tandem with previous research outlined in my literature review where
Tokat, Wikas (2004) looked at another financial crisis such as after the terrorist attacks on
the World Trade Centre and found that correlations also spiked at this period.
For the final section of my investigation of EM diversification I compared a purely DM index
portfolio with a portfolio consisting of DM and EM indexes. These portfolios were
denominated in Ireland so the home index was the ISEQ. The two portfolios were compared
on the basis of the risk and returns they offered over the 15 year time horizon and between
the three sub-periods. This information which is described in detail in Chapter 4 is
summarized by the graph in Figure 12, where R1 and R2 represent returns for the DM and
the “DM+EM” portfolios respectively. Furthermore, σ1 and σ2 show portfolio risk levels for
the DM and “DM+EM” portfolios in the form of standard deviation.
81
Figure 12: Irish Denominated Portfolios
It should be noted that there were a number of assumptions made in the portfolio
construction the largest share of wealth in each portfolio was taken to be invested in the
domestic index, the ISEQ, due the theory of imperfect international diversification described
by French, Porterba (1991) whereby investors expect domestic returns to be higher than
foreign ones. As well as this there were a number of factors presumed not to significantly
affect the risk and returns of the portfolio. In tandem with the findings of French, Porterba
(1991) I underwent my investigation with the assumption that transaction costs, market
liquidity, taxes and government limitations were insignificant in negatively affecting the risk
and returns of the portfolio. I also included all 11 DM indexes and 22 indexes in creating the
portfolios as the higher the number of countries and the more regions that are taken into
consideration, the more favourable is the risk return combination of the portfolio (Levy,
Sarnat 1970).
-0.02
-0.01
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
1995-2010 1995-1999 2000-2006 2007-2010
R1
R2
σ1
σ2
82
The majority of the time periods I examined showed results in tandem with previous
research. For the 15 year time horizon each of the sub-periods, 1995 – 1999 and 2000 –
2006 and 2007 – 2010 the portfolio consisting of DM and EM indexes displayed higher,
superior returns in comparison to the portfolio comprising of only DM indexes as can be
seen from Figure 12. As well as this lower portfolio risk levels were seen for the portfolio
consisting of DM and EM indexes for the 15 year time horizon and the first two sub-periods
from 1995 – 1999 and from 2000 – 2006. This is pursuant to previous research such as that
by Divech ,Drach (1992) which was outlined in my literature review and also showed that
portfolios consisting of DM and EM securities displayed higher returns and lower risk levels
compared to purely DM portfolios. However one finding I made went against previous
research. In relation to the period spanning the height of the global financial crisis I found
that the portfolio consisting of DM and EM securities displayed a higher level of risk
compared to the purely DM portfolio. The reasons that this anomaly has occurred are
almost definitely linked to the financial crisis and could be considered a further area to
examine for future research.
Lastly the effect of the financial crisis on portfolio risk and return can be clearly seen from
Figure 12. In tandem with previous empirical findings it can be seen that in comparison with
the other two sub-periods, the portfolio in the period from 2007 – 2010 exhibits lower
returns and considerably higher risk levels for both portfolio types. This follows from
previous work by Tokat, Wikas (2004) who found that in the time spanning the the Mexican
Peso Crisis and the Asian Crisis, US investors exposure to EMs during these periods reduced
portfolio return and increased portfolio standard deviation.
83
Over short periods of time, stock market booms and bubbles, financial crises, and the cycle
of bull and bear markets may create a strong temptation for investors to believe that the
stock market's behaviour in any one period invalidates the long-term case for international
investing in. To derive the long-term benefits of investing in EMs investors should he
prepared for significant short-term deviations from reasonable long-term expectations.
“Efficient diversification and international asset allocation strategies can always be
identified using careful research” – (Odier, Solnik 1993)
There are a couple of areas where future research could be directed to get a better
understanding of diversification in the contemporary world economy as well as the benefits
it can present for the Irish investor. This researcher noticed that South Africa displayed
consistent growth across all the time periods that were examined. As well as this, despite
the global economic downturn South Africa exhibited the highest growth level out of all of
the 33 indexes with 672% periodic growth from 2007 – 2010. Furthermore, it was the only
country that showed negative correlations with the ISEQ and the S&P 100 for every time
period investigated. These finding lead this researcher to believe that there is considerable
diversification benefits to be gained from investing in South Africa, and that it would be
prudent to look at similar African markets as potential investments. Further areas for future
research will be discussed in my conclusions.
84
Chapter 6
Conclusions
85
From my findings I have shown that EMs do indeed still exhibit higher returns and risks
when compared to DMs. As well as this lower correlations can still be seen between DM and
EM indexes than amongst DM indexes. However correlations between DMs and EMs have
been shown to be increasing steadily since 1995 in line with global market integration. Over
the 15 year time horizon and the two sub-periods 1995 – 1999, 2000 – 2006 the Irish
denominated portfolio consisting of both DMs and EMs outperformed the DM portfolio in
terms of both risk and return. Interestingly, the DM portfolio actually presented less risk
than the EM and DM portfolio for the period of the financial crisis from 2007 – 2010.
However the portfolio with both DM and EM indexes still provided a higher return in
comparison. This means that allocation between DM and EM indexes in the portfolio would
likely come down to the individual investor’s risk tolerance.
Despite the findings made from my paper which have shown a steady increase in
correlations between DMs and EMs, it is my opinion that EMs are still a pivotal part of an
investor’s global portfolio who seeks to maximise return and minimise risk. According to a
recent IMF (2011) article the divergent growth prospects of EMs and DMs are here to stay.
It was also predicted in this piece that a rise in EM economic power will be seen to
strengthen and extend beyond the current financial crisis. One of the reasons that
divergence of growth might be forecasted by the IMF might be evident from my findings. If
the assumption that correlations between DM and EM indexes spike during periods of
financial crisis is correct then it might be prudent to expect that correlations between EMs
and DMs might deteriorate somewhat over the next couple of years as the world economy
reverts back to a neutral more normal state.
86
Growth in Emerging Market countries is outpacing developed countries so greatly that the
global economic landscape will be wholly altered over the next 15 years. According to the
World Bank by 2025, six major emerging economies, Brazil, China, India, Indonesia, South
Korea and Russia will account for more than half of all global growth, and the International
Monetary System will likely no longer be dominated by a single currency. Currently, the U.S.
dollar serves as the world's reserve currency but there has been a slow decline in its role
since the late 1990’s that was likely to continue.
The IMF’s latest global forecasts project that Emerging Economies will grow by 6.5% this
year (and an even higher 8% in Emerging Asia) compared with expected global growth of
4.5% according to a statement by the IMF Deputy Managing Director Naoyuki Shinohara in
April this year.
The World Bank said it expects a sharp divergence in growth to continue between the
Emerging Market nations and old-line rich powers like the United States and other G7
members: Britain, Canada, France, Germany, Italy and Japan. The World Bank also
estimated that Emerging Economies will grow on average by 4.7% a year between 2011 and
2025, twice the 2.3% growth rate likely to occur in advanced countries. By 2025, the United
States, the Euro area and China will constitute the world's three major "growth poles," the
World Bank said, providing stimulus to other countries through trade, finance and
technological developments and thus creating global demand for all of their currencies, not
just the dollar (Reuters).
87
In tandem with my findings and recommendation for future research, I found that a number
of financial services companies and experts have also identified South Africa as a very
enticing market for investors. In an article in Reuters (2007) it was highlighted that the rapid
growth levels previously seen in the traditional ‘major’ EMs of Brazil, Russia, India and China
(BRICs) were beginning to slow down. The article also pointed to the fact that some EM
investors were beginning to look at a new set of markets such as Vietnam, India, South
Africa, Turkey and Argentina (VISTA). The article also pointed to the fact that what helped
transform BRIC from just a simple acronym into an investment strategy was a combination
of similar fundamentals among the countries and the potential for rapid economic and
capital markets' expansion, at a time when global investors were becoming disenchanted
with low returns in U.S. assets. In an even more recent article in Reuters (2010) they
outlined another series of EMs that exhibit superior potential growth. According to HSBC
CEO Michael Geoghegan the most favourable EMs at moment are Colombia, Indonesia,
Vietnam, Egypt, Turkey and South Africa (CIVETS). It is this researcher’s opinion that the
future of investment in EMs lies in these CIVETS markets.
However there are also new risks to consider with this ubiquitous growth for EMs.
According to the previous IMF Managing Director overheating and inflationary pressures are
the most pressing issues, with strong domestic demand, output levels that are close to their
potential and high capital inflows. The main sources of inflationary pressure are narrowing
output gaps, rising commodity prices, and a wave of capital inflows. As has been evidenced
by the occurrence of the Asian Crisis in 1997 the need to effectively control large capital
inflows is imperative.
88
Appendices
89
Appendix 1 – Periodic Growth 1995 - 2010
Periodic Growth DMs Periodic Growth EMs UK 0.960569
BRAZIL 20.18846
US(S&P) 1.580357
RUSSIA 18.52336 US(DJ) 1.886404
INDIA 4.994233
FRA 1.141306
CHINA 4.112473 GER 2.295846
POLAND 6.36586
JAP -0.40018
CZECH 1.556994 IRE 0.527268
HUNG 16.67827
HK 1.766184
SLOVAK 3.239991
SING 1.786666
ARG 9.924505 AUS 1.059751
CHILE 4.317172
SWISS 1.475019
INDO 7.165141
PHILLI 6.539162
MALAY 1.041885
THAI -0.19846
TAIWAN 0.378406
MEXICO 24.07532
SAUDI 4.511459
TURKEY 225.6404
MORROC 2.599147
TUNISIA 3.748412
KUWAIT 7.675803
SAFRICA 5.529557
90
Appendix 2 – Periodic Growth 1995 - 1999
Periodic Growth DMs Periodic Growth EMs UK 1.003722
BRAZIL 2.395515
US(S&P) 1.631794
RUSSIA 2.858547 US(DJ) 1.577118
INDIA 0.392507
FRA 1.584022
CHINA 1.859669 GER 1.454813
POLAND 1.211686
JAP 0.032371
CZECH 0.049061 IRE 1.499455
HUNG 4.59278
HK 0.529055
SLOVAK 1.670041
SING 2.137911
ARG 0.656849 AUS 0.900545
CHILE 0.062058
SWISS 1.656284
INDO 0.208038
PHILLI 3.361735
Average 1.273372
MALAY 1.10733 Max 2.137911
THAI -0.69771
Min 0.032371
TAIWAN 0.167369
MEXICO 2.285066
SAUDI 0.39852
TURKEY 19.84607
MORROC 2.074225
TUNISIA 0.161144
KUWAIT 0.48164
SAFRICA 0.577422
Average 2.078162
Max 19.84607
Min -0.69771
91
Appendix 3 – Periodic Growth 2000 - 2006
Periodic Growth DMs Periodic Growth EMs UK -0.00761
BRAZIL 1.713753
US(S&P) 0.017096
RUSSIA 2.765968 US(DJ) 0.139172
INDIA 1.648634
FRA -0.02086
CHINA 0.742983 GER -0.03492
POLAND 1.598815
JAP -0.11842
CZECH 1.866498 IRE 0.91883
HUNG 1.601799
HK 0.285365
SLOVAK 0.121134
SING -0.2117
ARG 2.691566 AUS 0.082514
CHILE 1.339286
SWISS 0.274274
INDO 1.837213
PHILLI -0.07938
Average 0.120341
THAI 0.42354 Max 0.91883
TAIWAN -0.19715
Min -0.2117
MEXICO 3.016041
SAUDI 2.985777
TURKEY 1.340261
MORROC 0.595356
TUNISIA 2.872036
KUWAIT 3.07971
SAFRICA 2.774537
Average 1.654209
Max 3.07971
Min -0.19715
92
Appendix 4 – Periodic Growth 2007 - 2010
Periodic Growth DMs Periodic Growth EMs UK -0.04887
BRAZIL 0.552471
US(S&P) -0.12557
RUSSIA 1.342583 US(DJ) -0.08273
INDIA 0.455483
FRA -0.32158
CHINA 0.007803 GER 0.018424
POLAND -0.12955
JAP -0.41157
CZECH -0.26368 IRE -0.68658
HUNG -0.11357
HK 0.145676
SLOVAK -0.04689 SING -0.00192
ARG 0.701691
AUS -0.04639
CHILE 0.701918 SWISS -0.29546
INDO 1.107552
PHILLI 0.216346
Average -0.16878
THAI 0.579047 Max 0.145676
TAIWAN 0.165314
Min -0.68658
MEXICO 0.398719
SAUDI -0.06164
TURKEY 0.602729
MORROC -0.37738
SAFRICA 6.722571
Average 0.661133
Max 6.722571
Min -0.37738
93
Appendix 5 – US Correlations 1995 – 2010
US Correlations with DMs US Correlations with EM
UK 0.806958
BRAZIL 0.637836 US(S&P) 0.943563
RUSSIA 0.044914
FRA 0.760437
INDIA 0.39361 GER 0.759781
CHINA 0.185321
JAP 0.518785
POLAND 0.512402 IRE 0.658746
CZECH 0.435419
HK 0.659514
HUNG 0.57458 SING 0.869191
SLOVAK 0.682452
AUS 0.805563
ARG 0.425809 SWISS 0.715267
CHILE 0.494458
INDO 0.456079
Average 0.749781
PHILLI 0.706407 Max 0.943563
MALAY 0.090213
Min 0.518785
THAI 0.494314
TAIWAN 0.481068
MEXICO 0.633781
SAUDI 0.175494
TURKEY 0.461876
MORROC 0.691616
TUNISIA 0.020511
KUWAIT -0.03414
SAFRICA -0.07229
Average 0.385988
Max 0.706407
Min -0.07229
94
Appendix 6 – US Correlations 1995 – 1999
US Correlations with DMs US Correlations with EMs
UK 0.695213
BRAZIL 0.625953 US(S&P) 0.917133
RUSSIA 0.015772
FRA 0.621881
INDIA 0.091908 GER 0.676276
CHINA 0.026473
JAP 0.461756
POLAND 0.404521 IRE 0.638838
CZECH 0.257251
HK 0.718582
HUNG 0.550341 SING 0.833449
SLOVAK 0.631897
AUS 0.691122
ARG 0.708767 SWISS 0.615752
CHILE 0.606864
INDO 0.513736
Average 0.687
PHILLI 0.668303 Max 0.917133
MALAY 0.148352
Min 0.461756
THAI 0.559452
TAIWAN 0.453409
MEXICO 0.679977
SAUDI -0.01704
TURKEY 0.269961
MORROC 0.642216
TUNISIA 0.03982
KUWAIT 0.026563
SAFRICA -0.07055
Average 0.356088
Max 0.708767
Min -0.07055
95
Appendix 7 – US Correlations 2000 - 2006
US Correlations with DMs US Correlations with EMs UK 0.826567
BRAZIL 0.632306
US(S&P) 0.92595
RUSSIA 0.073144 FRA 0.751247
INDIA 0.374199
GER 0.760762
CHINA 0.039918 JAP 0.417624
POLAND 0.432889
IRE 0.702038
CZECH 0.305784 HK 0.599409
HUNG 0.447857
SING 0.819314
SLOVAK 0.691718 AUS 0.829007
ARG 0.149725
SWISS 0.730476
CHILE 0.537136
INDO 0.257274
Average 0.736239
PHILLI 0.677075 Max 0.92595
MALAY 0.139922
Min 0.417624
THAI 0.499605
TAIWAN 0.435754
MEXICO 0.493082
SAUDI -0.01403
TURKEY 0.560366
MORROC 0.627763
TUNISIA 0.047604
KUWAIT -0.12311
SAFRICA -0.09768
Average 0.326741
Max 0.691718
Min -0.12311
96
Appendix 8 – US Correlations 2007 - 2010
US Correlations with DMs US Correlations with EMs UK 0.858756
BRAZIL 0.738809
US(S&P) 0.978502
RUSSIA -0.10475 FRA 0.881822
INDIA 0.718239
GER 0.857779
CHINA 0.468147 JAP 0.692716
POLAND 0.820452
IRE 0.670384
CZECH 0.7965 HK 0.701629
HUNG 0.781259
SING 0.945303
SLOVAK 0.733405 AUS 0.858705
ARG 0.67859
SWISS 0.827403
CHILE 0.42094
INDO 0.659224
Average 0.8273
PHILLI 0.878929 Max 0.978502
THAI 0.576007
Min 0.670384
TAIWAN 0.647235
MEXICO 0.791705
SAUDI 0.509329
TURKEY 0.65432
MORROC 0.787412
SAFRICA -0.01839
Average 0.607229
Max 0.878929
Min -0.10475
97
Appendix 9 - DM Portfolio 2000 – 2006
Appendix 10 – DM Portfolio 2007 – 2010
98
Appendix 11 – DM+EM Portfolio Covariance Matrix 1995 – 1999
99
Appendix 12 – DM+EM Portfolio Bordered Covariance Matrix 1995 – 1999
100
Appendix 13 – DM+EM Portfolio Covariance Matrix 2000 – 2006
101
Appendix 14 – DM+EM Portfolio Bordered Covariance Matrix 2000 - 2006
102
Appendix 15 – DM+EM Portfolio Covariance Matrix 2007 – 2010
103
Appendix 16 – DM+EM Portfolio Bordered Covariance Matrix 2007- 2010
104
Appendix 17 - Risk Return: 1995-2010
Appendix 18 - Risk Return:
1995-1999
0
0.02
0.04
0.06
0.08
0.1
0.12
DMs EMs
Mean
Std.Dev
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
DMs EMs
Mean
Std.Dev
105
Appendix 19 - Risk Return:
2000 - 2006
Appendix 20 - Risk Return:
2007 - 2010
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
DMs EMs
Mean
Std.Dev
-0.01
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
DMs EMs
Mean
Std.Dev
106
Appendix 21 – Periodic Growth – 1995 - 2010
Appendix 22 – Periodic Growth – 1995 - 1999
0
2
4
6
8
10
12
14
16
18
20
DMs EMs
Growth
Growth
0
0.5
1
1.5
2
2.5
DMs EMs
Growth
Growth
107
Appendix 23 – Periodic Growth – 2000 - 2006
Appendix 24 – Periodic Growth – 2007 - 2010
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
DMs EMs
Growth
Growth
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
DMs EMs
Growth
Growth
108
Appendix 25 - Correlations: 1995 - 2010
Appendix 26 - Correlations:
1995 - 1999
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
ISEQ-DM ISEQ-EM
Corr
Corr
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
ISEQ-DM ISEQ-EM
Corr
Corr
109
Appendix 27 - Correlations:
2000 - 2006
Appendix 28 - Correlations:
2007 - 2010
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
ISEQ-DM ISEQ-EM
Corr
Corr
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
ISEQ-DM ISEQ-EM
Corr
Corr
110
Appendix 29 - Portfolio Risk &
Return
Appendix 30 - Portfolio Risk
-0.015
-0.01
-0.005
0
0.005
0.01
0.015
0.02
1995-2010 1995-1999 2000-2006 2007-2010
R1
R2
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
1995-2010 1995-1999 2000-2006 2007-2010
σ1
σ2
111
Bibliography
112
Journals:
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Books
Bodie, Z. Kane, A. Marcus, A. ‘Investments’. 8th Edition. McGraw Hill. 2010
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Foundation for Research in Economics, Blackwell Publishing. 1959, 1991.
Khanna, T. Palepu, K. ‘Winning in Emerging Markets’. Harvard Business Press. 2010
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Websites:
http://www.forbes.com/
http://www.ifc.org/
http://www.worldbank.org/
http://www.bloomberg.com/
http://www.imf.org
Articles:
IMF Magazine. ‘Emerging Markets Tackle Risks, Cement Global Power’. April 18th 2011.
Somerville, G. Adler, L. Reuters. ‘Emerging Markets Reshaping Landscape’. 11th May 2011.
Rodrigues, V. Reuters. ‘Emerging FX View-Investors may trade off BRIC for VISTA’. 13th July
2007.
Slater, S. Homes, D. ‘After BRICs, look to CIVETS for growth - HSBC CEO’. April 27th 2010.
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