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.

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“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

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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.

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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.

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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:

Abumustafa, N. ‘Risk Diversifications in Emerging Economies’. Risk Management. February

2007. pp 36-43.

Barry, C. Peavy III, J. Rodriguez, M. ‘Performance Characteristics of Emerging Capital

Markets’. Financial Analysts Journal. 1998. pp 9-17.

Bekaert, G. Harvey, C. ‘Emerging Equity Market Volatility’. National Bureau of Economic

Research. October 1995.

Bekaert, G. Harvey, C. ‘Emerging Markets Finance’. Journal of Empirical Finance. October

2003. pp 3-55.

Burmeister, E. Roll, R. Ross, S. ‘A Practitioner’s Guide to the Arbitrage Pricing Theory’.

Finanzmarkt und Portfolio Management. 1994.

Chen, N. Roll, R. Ross, S. ‘Economic Forces and the Stock Market’. The Journal of Business.

July 1986. pp 383-403.

Divecha,A. Drach, J. Stefek, D. ‘Emerging Markets: A Quantitative Perspective’. Journal of

Portfolio Management. October 1992. pp 41-50.

Errunza, V. ‘Emerging Markets: A New Opportunity for Improving Global Portfolio

Performance’. Financial Analysts Journal. October 1983. pp 51-58.

Errunza, V. Padmanabhan, P. ‘Further Evidence on the Benefits of Portfolio Investments in

Emerging Markets’. Financial Analysts Journal. July 1988. pp 76-78.

113

Fama, E. French, K. ‘Permanent and Temporary Components of Stock Prices’. Journal of

Political Economy. April 1988. pp 246-273.

Fifield, S. Power, D. Sinclair, C. ‘Emerging Stock Markets: A More Realistic Assessment of the

Gains from Diversification’. Applied Financial Economics. December 2002. pp 213-229.

French, K. Porterba, J. ‘Investor Diversification and International Equity Markets’. National

Bureau of Economic Research. May 1991. pp 222-226.

Grubel, H. ‘Internationally Diversified Portfolios: Welfare Gains and Capital Flows’. The

American Economic Review. December 1968. pp 1299-1314.

Harvey, C. ‘Predictable Risk and Returns in Emerging Markets’. National Bureau of Economic

Research. January 1994.

Harvey, C. Bekaert, G. Erb, C. Viskanta, T. ‘What Matters for Emerging Equity Market

Investments’. Emerging Markets Quarterly. Summer 1997.

Lessard, D. ‘International Portfolio Diversification: A Multivariate Analysis for a Group of

Latin American Countries’. The Journal of Finance. June 1973. pp 619-633.

R. Lessard, D. ‘World, Country, and Industry Relationships in Equity Returns: Implications for

Risk Reduction through International Diversification’. Financial Analysts Journal. February

1976. pp 32-38.

Levy, H. Sarnat, M. ‘International Diversification of Investment Portfolios’. The American

Economic Review. September 1970. pp 668-675.

Lintner, J. ‘Security Prices, Risk, and Maximal Gains from Diversification’. The Journal of

Finance. December 1965. pp 587-615.

114

Markowitz, H. ‘Portfolio selection’. The Journal of Finance. March 1952. pp 77-91.

Markowitz, H. ‘The Early History of Portfolio Theory’. Financial Analysts Journal. August

1999. pp 5-16.

Odier, P. Solnik, O. ‘Lessons for International Asset Allocation’. Financial Analysts Journal.

April 1993. pp 63-77.

Rubenstein, M. ‘Markowitz's Portfolio Selection: A Fifty-Year Retrospective’. The Journal of

Finance. June 2002. pp 1041-1045.

Sharpe, W. ‘Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk’.

The Journal of Finance. September 1964. pp 425-442.

Sharpe, W. ‘Portfolio Analysis’. The Journal of Financial and Quantitative Analysis. June

1967. pp 76-84.

Solnik, B. ‘Why Not Diversify Internationally Rather than Domestically? ’. Financial Analysts

Journal. August 1974. pp 48-54.

Tokat, Y. Wicas, N. ‘Investing in Emerging Stock Markets’. The Journal of Wealth

Management. Fall 2004.

Books

Bodie, Z. Kane, A. Marcus, A. ‘Investments’. 8th Edition. McGraw Hill. 2010

Markowitz, H. ‘Portfolio Selection: Efficient Diversification of Investments’. Cowles

Foundation for Research in Economics, Blackwell Publishing. 1959, 1991.

Khanna, T. Palepu, K. ‘Winning in Emerging Markets’. Harvard Business Press. 2010

115

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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