ERASMUS UNIVERSITY ROTTERDAM
ERASMUS SCHOOL OF ECONOMICS
MSc Economics & Business
Master Specialization Financial Economics
Capital Asset Pricing in Emerging Markets:
An Empirical Investigation
Author:
H. A. Barahmeh
EUR study number:
276622hb
Thesis supervisor :
Dr. R.C.J. Zwinkels
Finish date:
March 2010
Hafez Barahmeh 2010
ACKNOWLEDGEMENTS
This thesis is the result of my research on the Capital Asset Pricing Model in
emerging markets. The thesis is written for the completion of the Master’s of Science
in Economics & Business at the Erasmus University Rotterdam.
I would first like to acknowledge and thank my thesis supervisor Dr. R.C.J Zwinkels.
I am very grateful for his support, guidance and patience during the writing of my
thesis. I would also like to thank my family and friends for their support and love
during my time at the university. All of them helped me to successfully complete my
Master’s degree.
Hafez Barahmeh,
Rotterdam, March 2010
NON-PLAGIARISM STATEMENT
By submitting this thesis the author declares to have written this thesis completely by himself/herself, and not to
have used sources or resources other than the ones mentioned. All sources used, quotes and citations that were
literally taken from publications, or that were in close accordance with the meaning of those publications, are
indicated as such.
COPYRIGHT STATEMENT
The author has copyright of this thesis, but also acknowledges the intellectual copyright of contributions made by
the thesis supervisor, which may include important research ideas and data. Author and thesis supervisor will have
made clear agreements about issues such as confidentiality.
Electronic versions of the thesis are in principle available for inclusion in any EUR thesis database and repository,
such as the Master Thesis Repository of the Erasmus University Rotterdam
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Capital Asset Pricing in Emerging Market: an Empirical Investigation
ABSTRACT
This paper studies the relatively understudied financial markets Egypt, Israel,
Morocco and Turkey within the context of several variants of the capital asset pricing model.
While there are many studies dealing with equity markets, risk, and returns in emerging
economies, only a small number of the examine the countries used in this study. The countries
analyzed in this study are emerging markets which have different characteristic compared to
established financial markets. The weekly stock returns were investigated by testing different
versions of the CAPM, namely the Global CAPM, the Three Moment CAPM, the Four
Moment CAPM and the local CAPM. Moreover, the models were tested under differing
financial conditions. The study used weekly return from 516 companies listed on the CASE,
ISE, TASE, CSE from January 2001 to September 2009.
Ordinary Least Squares (OLS) regression was used to estimate betas. The results do
not support the hypothesis that there exists a positive risk return relationship in the emerging
markets. The CAPM’s prediction for the intercept is that it should equal zero. The findings of
this study do not clearly reject the above hypothesis. In addition, the findings in this study do
not clearly support multifactor models. The findings in this study suggests that the CAPM
cannot be clearly rejected and shows some merit in emerging markets. Moreover, emerging
markets have interesting diversification benefits for international investors.
The model that performs best in emerging markets, depends on which country and
which time period is considered. The CAPM models do not perform well in explaining
emerging stock markets returns. Interestingly the CAPM performs better during times of
financial crisis, in particular the Local CAPM.
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Hafez Barahmeh 2010
TABLE OF CONTENTS
ACKNOWLEDGEMENTS ................................................................................................................... 2
ABSTRACT ........................................................................................................................................... 3
TABLE OF CONTENTS ....................................................................................................................... 4
LIST OF TABLES ................................................................................................................................. 6
CHAPTER 1.INTRODUCTION ............................................................................................................ 7
CHAPTER 2. DEVELOPED MARKETS VS. EMERGING MARKETS .......................................... 10
Differences between developed and emerging markets ...................................................... 10
Market size ...................................................................................................................... 10
Market efficiency............................................................................................................. 11
Market Transparency ....................................................................................................... 11
Market liquidity ............................................................................................................... 12
Market Integration ........................................................................................................... 12
History of the CAPM .......................................................................................................... 13
Difficulties with the CAPM............................................................................................. 14
Criticism of beta .............................................................................................................. 15
Versions of the Capital Asset Pricing model ....................................................................... 17
The traditional CAPM ..................................................................................................... 17
Emerging market CAPM models .................................................................................... 17
The Global CAPM........................................................................................................... 17
The three moment CAPM ............................................................................................... 18
The four moment CAPM ................................................................................................. 19
The local CAPM .............................................................................................................. 19
CHAPTER 3. RESEARCH METHODOLOGY .................................................................................. 21
Data selection ...................................................................................................................... 21
Stock market activity ....................................................................................................... 22
Methodology ....................................................................................................................... 22
Global risk free rate ............................................................................................................. 22
Country risk premium...................................................................................................... 23
Local risk free rate ........................................................................................................... 23
Market portfolio............................................................................................................... 23
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Capital Asset Pricing in Emerging Market: an Empirical Investigation
Beta estimation ................................................................................................................ 24
EMPIRICAL RESULTS AND INTERPRETATION OF THE FINDINGS ....................................... 25
Emerging market data ......................................................................................................... 25
Investigation of the data ...................................................................................................... 26
Market correlations.............................................................................................................. 29
Country risk premium ......................................................................................................... 30
Excess market returns .......................................................................................................... 31
Beta estimates ...................................................................................................................... 32
CONCLUSION .................................................................................................................................... 42
REFERENCES ..................................................................................................................................... 44
OVERVIEW OF THE APPENDICES ................................................................................................. 48
Appendix A .......................................................................................................................................... 49
Appendix B........................................................................................................................................... 51
Appendix C........................................................................................................................................... 54
Appendix D .......................................................................................................................................... 55
Appendix E ........................................................................................................................................... 61
Appendix F ........................................................................................................................................... 63
Appendix G .......................................................................................................................................... 65
Appendix H .......................................................................................................................................... 69
Appendix I ............................................................................................................................................ 70
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Hafez Barahmeh 2010
LIST OF TABLES
Table
Page
Table 1.
Summary statistics of individual company data
27
Table 2.
Descriptive statistics for the market returns and the risk-free rate
28
series in four emerging markets
Table 3.
Historical correlations matrix of the four emerging stock markets
30
and the world market
Table 4.
Credit Default Swap (CDS) data
31
Table 5.
Excess market returns of four emerging markets
32
Table 6.
Minimum and Maximum values of the estimated betas
33
Table 7.
Global CAPM
35
Table 8.
Three Moment CAPM
37
Table 9.
Four Moment CAPM
39
Table 10.
Local CAPM
41
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Capital Asset Pricing in Emerging Market: an Empirical Investigation
CHAPTER 1.INTRODUCTION
Investors are always looking for new investment opportunities around the world.
During the last few years, more and more investors are focusing on emerging equity markets.
One reason for this change in investment behavior is probably the high returns offered by
these markets. Furthermore many of these markets provide interesting diversification benefits.
However, some problems arise with the upcoming interest in these emerging markets. One of
the main difficulties is the problem of the valuation of the investments.
Financial investors use many valuation techniques to assess the risk and return on their
investments. One of the most commonly used valuation techniques by financial practitioners
in developed markets is the Capital Asset Pricing Model (CAPM) as developed by Sharpe
(1964), Lintner (1965) and Mossin (1966). The CAPM suggests a relationship between the
risk and the expected return of an investment. The expected return on an asset above the riskfree rate is linearly related to the non-diversifiable risk as measured by beta. According to the
CAPM, the market beta alone is sufficient to explain security return. Although the CAPM is
the most widely used model as well as the basis of modern portfolio theory, evidence suggests
that there is some doubt about the models ability to explain the cross-section of stock returns.
Several researchers (Banz, 1981; Fama and French, 1992; Jagannathan and Wang, 1996;
Lettau and Ludvigson, 2001) show evidence that the cross-section of returns cannot be
explained only by beta. For instance, Fama and French (1992) show that the size and book-tomarket ratios provide better explanations for the cross-section of stock returns than beta.
Research by Banz (1981) indicates that average stock returns are better explained by firm
size, than by beta. Furthermore, Roll (1977) states that the CAPM cannot be tested since the
market portfolio should encompass all potential investment options, and not just stocks.
Despite its debatable practical value, the model gives great insight in the risk and return
relationship.
Besides the troubles with the CAPM in developed markets, when applied to emerging
markets the situation gets even more complicated. Most valuation techniques applied in
developed markets cannot be straightforwardly applied in emerging equity markets. The main
principles of developed markets are diversification and transparency, which are not present in
emerging markets. Financial investors do not agree about the existence of efficiency in
emerging markets, since these equity markets are small and concentrated. In addition, stock
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Hafez Barahmeh 2010
market prices are scarce and unreliable. Consequently, the straight application of the CAPM
in emerging markets is controversial (Pereiro, 2002). In order to try to solve some of the
problems associated with the application of the CAPM in emerging markets, several authors
have made modifications to the CAPM (Pereiro, 2001; Godfrey and Espinosa, 1996). In
addition, to account for the problems with the emerging stock market data, a number of
corrections are available, such as a correction for thin trading (Omran, 2007).
The validity of the CAPM in emerging markets is not completely verified by
empirical evidence. According to Erb, Harvey and Viskanta (1996), the CAPM shows some
merit in developed markets, but in emerging markets the evidence is mixed. Surveys by
Harvey (1995) and Estrada (2000) show that the betas of emerging markets are not correlated
with returns when computed against the world market. Moreover, the emerging equity
markets are highly volatile and beta values appear to be too low, these beta values result in
cost of equity capital values that are not considered as reasonable by most investors. This had
led to the idea that the CAPM is inapplicable in the case of emerging market stock exchanges.
However, since investments in emerging markets are increasing and a there is a growing
demand for appropriate valuation tools, this research will focus on emerging markets.
In the light of the increasing investments in emerging equity markets, it is important
to appropriately appraise the investment opportunities. The goal of this article is to examine
which asset pricing model is best able to explain the stock market returns of emerging equity
capital markets of Egypt, Israel, Morocco and Turkey1. Furthermore, it is examined whether
the CAPM or different versions of the CAPM are applicable in the emerging capital markets.
The performance of four asset pricing models are evaluated. Tests are conducted for a period
of 8 years (January 2001- September 2009), which is characterized by severe return volatility
(covering several crisis, 9-11,Credit Crisis). These market return characteristics make it
possible to empirically investigate the pricing models on differing financial conditions. There
is not much literature about the financial markets used in this study. The goal is to widen the
theoretical analysis of these markets by using modern finance theory and provide useful
insights for future analysis of these emerging markets.
The main question in this article is whether the CAPM is applicable in emerging
markets, and if so, which model performs best. Following the methodology used by Fama &
Macbeth (1973), historical returns of the individual stocks are calculated and beta is estimated
using the Ordinary Least Squares (OLS) regression method. For each company in the sample
the returns are regressed on the stock index to estimate beta. It is expected that the CAPM
1
After an investigation of the Middle Eastern and North African stock markets, these stock markets
were chosen based on their high past returns, their liquidity and the availability of data.
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Capital Asset Pricing in Emerging Market: an Empirical Investigation
does work in emerging markets therefore a linear relationship between the expected return on
a security and its risk is expected. Moreover higher risk is expected to be associated with
higher expected return and risk aversion. In light of previous research multifactor models are
expected to add information.
This paper makes a few contributions to the literature. First, few papers investigate
the asset pricing models on differing financial conditions in the finance literature. This paper
sheds light on the risk-return relationship during periods of financial crisis. Second, this
investigation covers several Middle Eastern markets. There has been few research on asset
pricing models in these markets. Moreover, these countries have interesting diversification
possibilities. Third, this comparison covers emerging markets, which are usually not the main
topic of studies of the CAPM. This research may have important implications for investors
interested in opportunities available in these markets as well as for academics studying the
international aspects of finance. Investors will have more guidance as to which risk-free rate
and market proxy they should use when valuing their investments, so they can better
determine their cost of equity capital values. For scholars, the insights gained in this research
are an addition to the research on the applicability and usefulness of the CAPM and add to the
knowledge of the workings of emerging equity markets. The results indicate that the CAPM
cannot be clearly rejected when applied to emerging markets. However, the evidence cannot
be seen as support of the CAPM. The results also show that the model that performs best in
emerging markets, depends on which country and which time period is considered. The
CAPM models do not perform well in explaining emerging stock markets returns. Moreover,
evidence indicates that the risk return relationship is not always positive in emerging markets.
The remainder of this paper is organized as follows. Chapter 2 presents the emerging
countries financial systems and gives a detailed description of the CAPM, Chapter 3 describes
the data and methodology. The empirical results are presented in Chapter 4. The final section
contains concluding comments.
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Hafez Barahmeh 2010
CHAPTER 2. DEVELOPED MARKETS VS. EMERGING
MARKETS
The purpose of this section is to describe some key characteristics of emerging capital
markets and compare them with those of developed markets. Furthermore the several versions
of the CAPM are described.
DIFFERENCES BETWEEN DEVELOPED AND EMERGING MARKETS
Since the 1980s emerging markets have drawn the interest of investors. Before that
time capital flows into the emerging markets were small, as a result of the high market risk
and volatility and the lack of financial products and services available for foreign investors
(Hoyer-Ellefsen, 2004). A movement of economic liberalization and privatizations throughout
these emerging markets lead to an increase of emerging market assets that were available for
foreign investors. These emerging market securities were attractive for portfolio investment
purposes. Several countries are considered to be in the transition to higher levels of economic
development. The countries are considered as ‘emerging markets’ by the International
Finance Corporation (IFC) of The World Bank. Among these countries are the countries used
in this study; Egypt, Israel, Morocco and Turkey. Some of the key dimensions on which
developed markets differ from emerging markets are market size, openness, efficiency,
transparency, and liquidity (Hoyer-Ellefsen, 2004). However, emerging markets can differ
considerably among themselves.
MARKET SIZE
Developed markets differ from emerging markets in two ways. First, the overall size
of the emerging market economies is much smaller in terms of GDP relative to developed
markets. Hoyer-Ellefsen (2004) shows that the GDP of developed markets is more than six
times larger. Secondly, the size of the financial markets of emerging markets in relation to
their economies as a whole is much smaller. Pereiro (2001) compares the size of the stock
market to the GNP and finds that the importance of the stock markets in emerging markets in
relation to the markets economy is small. Levine (1997) shows that citizens of the poorest
countries (bottom 25% on the basis of income per capita) held just one-third of the yearly
income in formal financial intermediaries, compared to two-third of their yearly income when
considering citizens of the richest countries (top 25% on the basis of income per capita).
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Capital Asset Pricing in Emerging Market: an Empirical Investigation
Although, developed and emerging markets seem to display a similar number of listed firms,
the market capitalization of the listing for developed markets is noticeably larger than that of
emerging markets (Hoyer-Ellefsen, 2004).
MARKET EFFICIENCY
The degree to which the present price of a security reflects all the information that is
known about the assets underlying security is referred to as market efficiency. In an efficient
market new information is quickly reflected in the prices. According to Hoyer-Ellefsen (2004)
most developed markets exhibit the “weak form” and “semi-strong” forms of market
efficiency. These forms state that past prices do not predict future returns, and asset prices
adjust quickly to the release of new information. On the other hand, emerging equity markets
are viewed as less efficient compared to developed equity markets. Mainly because stocks are
less often traded, and less analysts follow the market. As a result new information, such as
earnings announcements or dividend changes are not reflected in the prices immediately.
Bruner et al. (2003) found evidence for a “weak form” of market efficiency in emerging
markets. Using regression analysis on past returns to predict current returns Hoyer-Ellefsen
(2004) shows that more than half his sample of emerging markets did not even show the weak
form of market efficiency. This was expected given the relatively lower availability of market
information and higher corruption. Pereiro (2001) states that efficiency is hampered as a
result of a small number of listed companies, and lower market volume and capitalization of
the emerging stock markets.
MARKET TRANSPARENCY
All markets differ in their degrees of transparency, but emerging markets are less
likely to be transparent than developed markets. The degree of transparency is of great
influence of the analysts and investors ability to collect information to develop expectations to
facilitate investment decision making. Emerging stock markets are highly concentrated and
activity often concentrates around a few stocks, which makes the markets prone to price
manipulation by investors (Pereiro, 2001). Concentration makes investing difficult, and
makes the market less efficient as a whole (Pereiro, 2001). It is also important to look at the
accuracy of the information that is available. Disclosure requirements in emerging markets
are often less strict. Consequently, the accounting information is less comprehensive and less
detailed. There are several more factors that can influence data reliability in emerging markets
according to Pereiro (2001); inflation, exchange risk, the chance of expropriation, unstable
governments, changing laws, weak central banks which allow for currency manipulations,
restrictions to capital inflows or outflows, corruption in both the private and public sectors.
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Hafez Barahmeh 2010
Information asymmetry is also more prevalent in emerging markets, since not all information
is equally available for all investors.
MARKET LIQUIDITY
An important difference between emerging and developed markets is the speed at
which investors are able to get in and out of their investments, thus convert their assets into
purchasing power at agreed prices (Levine, 1997). Emerging equity markets are often less
liquid than developed markets, although they can vary considerably in their liquidity. As
mentioned before, less developed stock markets have less market capitalization, lower trading
volume and are more concentrated. These factors make it more difficult for investors to make
large trades without changing the stock price and to buy or sell large quantities of stocks
because less seller and buyers are available in the market. According to Levine (1997) high
trading costs can also be detrimental for liquidity.
MARKET INTEGRATION
The level of capital market integration is important to consider when investing in
emerging markets. There are several determinants of market integration, for instance; capital
controls, taxes and regulations, restrictions on foreign capital ownership. In some countries
foreign investors may not be permitted to invest in all the listed companies, furthermore their
ownership stake may also be limited (Benserud and Austgulen, 2006). According to Burner et
al., the level of market integration effects the choice of which CAPM model to use in the
regression (a global or local index). Mirsha and O’brien (2005) suggest that CAPM estimates
can differ substantially according to whether a local or global market index is used when
considering emerging markets.
The classical CAPM postulates that the risk free rate and the company’s risk premium
are sufficient to derive the opportunity cost of equity capital (the expected return). This model
is best fit for developed markets which are efficient and have numerous buyers and sellers of
financial assets. When dealing with emerging markets, the valuation of financial assets
becomes more complicated and the traditional CAPM model is not structured to deal with
emerging market conditions. As mentioned before, the emerging markets are smaller, less
efficient, less transparent, less liquid and measures of market integration are different.
Because of these characteristics the CAPM is less easily applicable in emerging markets. In
the CAPM, the covariance of a stock’s return with another asset’s return is important for the
investors rather than the total variance of an asset. It is important for the risk averse investor
to hold a well diversified portfolio in order to reduce the total risk of the portfolio. This is a
12
Capital Asset Pricing in Emerging Market: an Empirical Investigation
more difficult endeavor in emerging markets where diversification is not always perfect.
Hereafter the history of the CAPM and several version of the CAPM will be discussed.
HISTORY OF THE CAPM
Modern portfolio theory was first developed by Markowitz, who constructed the
mean-variance model. The model was designed to construct the optimal portfolio based on
the idea that risk and return have a positive relationship. Markowitz showed that stocks are
related to each other and risk can be decreased through diversification. The asset pricing
theory framework is designed to identify and measure risk in addition to assigning rewards
for risk bearing. The theory helps us understand why expected returns change through time.
The CAPM model was derived from this framework. The CAPM model is one of the
most influential and fundamental models used in financial economics. Basically, it examines
the relationship between the return and the risk of an asset. The model requires investors to be
compensated for the time value of money. The risk is reflected as the risk free rate and beta
respectively. The CAPM claims that the selection of the portfolio will depend upon the risk
free rate and the market return. A very important consequence of this model is the separation
theorem, which says that in the capital markets the risk has two components: diversifiable
(non-systematic) risk and non-diversifiable (systematic) risk. When pricing assets, the only
significant risk is systematic risk, since investors can get rid of the non-systematic risk
through diversification. Sharpe (1964) and Lintner (1965) show that beta is the true measure
of risk.
The asset pricing framework assumes that investors like higher rather than lower
expected returns, dislike risk, and hold well diversified portfolios. The model relies on the
assumptions that the individuals are risk averse and have homogenous expectations i.e. they
have the same expectations and estimates on mean, variance and covariance among the
returns and beta is the only measure of the market variance. A positive linear relationship
between the expected return and the beta of a security is implied by the CAPM. This means
that stocks with a larger beta will demand a higher expected return than a stock with a smaller
beta.
Stocks with a beta lower than 1 are considered passive stocks, and stocks with a beta
higher than 1 are considered aggressive and risky. Depending on the appetite toward risk,
investors would choose the stocks in their portfolio according to the value of beta. Today, the
CAPM remains popular among financial practitioners (Pereiro, 2002). The CAPM is both
used to determine the cost of capital for an investment project of a company as well as to
estimate the expected returns of stocks. Many financial decisions are based on the estimation
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Hafez Barahmeh 2010
of the cost of equity of the investment. According to Ben Naceur and Chaibi (2007). The cost
of equity is also important for portfolio management, project valuations, performance
evaluation and capital budgeting. The capital asset framework helps to illustrate the risk of a
particular project or acquisition, and assign a discount rate that is appropriate for that risk.
Projects that have a higher rate of return than predicted by the theory create value for
corporations. In addition it helps to identify overvalued and undervalues assets for portfolio
investments purposes. The classical version of the CAPM is the most widely used model to
estimate a firm’s weighted average cost of capital (WACC) (Graham and Harvey, 2001).
DIFFICULTIES WITH THE CAPM
Although the CAPM has dominated finance theory over thirty years, evidence shows
that the cross-section of stock returns cannot be described solely by the classical CAPM. The
CAPM had a lot of support before the discovery of anomalies in the 70’s. Further research on
the CAPM showed that the CAPM performed poorly in explaining stock market returns. Beta
alone does not seem sufficient to reflect the risk in the market. As a result researchers have
suggested adding other factors to the model to complete the beta in explaining stock price
movements. Evidence was found that stock market returns are related to some fundamental
factors such as size, book-to-market equity and momentum (Banz, 1981;Rosenberg et al.,
1985; Jegadeesh and Titman, 1993). Other studies have shown that average stock returns are
related to earnings/price ratios (Basu, 1983), to past sales growth (Lakonishok et al, 1994) and
display long term reversals (De Bondt and Thaler, 1985).
To address the anomalies that have been found, alternative models were examined to
explain the cross-section of stock returns. It has to be mentioned that some of these anomalies
could also be a result of the survivorship bias. This bias is created because data of bankrupt
companies are no longer available in databases.
These models take three different directions (Ben Naceur and Chaibi, 2007). The first
directions are the multifactor models that add a factor to the market return, such as the Fama
and French (1993) model, in which two other variables are added, the return of high book to
market value minus low book to market value stocks (HML) and the return on small minus
big stocks (SMB). Second, a different asset pricing theory, the Arbitrage Pricing Theory
(APT). Thirdly, the non-parametric models that criticize the linearity of the CAPM. These
separate directions will be briefly explained hereafter.
Basu (1977) proposes that other factors should be considered besides beta. He states
that the price earnings ratio has a great influence on the market return. Fama and French
(1993) found that size and book-to-market provide a better explanation of stock market
returns than the CAPM. This resulted in the extension of the one factor model to a three factor
14
Capital Asset Pricing in Emerging Market: an Empirical Investigation
model, including average stock market sensitivities to size and book-to-market ratio. The
three factor pricing model captures most of the market anomalies except the momentum
anomaly (Fama and French, 1996). Carhart (1997) proposes a four factor model; he adds
momentum to the Fama and French (1993) model as a result of the findings of Jegadeesh and
Titman (1993, 2001). Their findings suggest that momentum trading strategies can exploit the
phenomenon that stocks that perform the best (worst) over a 3-12 month period tend to
continue to perform well (poorly) over the subsequent 3-12 month period. These multifactor
models tend to address some criticisms of the classical CAPM, however these additional
factors are not motivated by theory (Ben Naceur and Chaibi, 2007)
The APT allows for the individual modeling of macro-economic factors, such as
inflation, sovereign, political and exchange risk. This way, typical components of country risk
can be modeled as explanatory factors. Evidence shows that in a quasi-efficient market, APT
shows a better predictive power than the CAPM (Copeland et al, 1994). The main problem
with using APT in emerging markets is the availability of data. Most of the analysts dealing
with emerging economies are confronted with data series that are usually incomplete,
extremely short, very volatile, and highly unreliable (pereiro, 2002). Only 8% of the
corporations and financial advisors use APT when calculating the cost of equity capital
(Pereiro 2001)
Non-parametrical models include additional moments into the CAPM (Harvey and
Siddique, 2000; Dittmar, 2002). Dittmar (2002) added kurtosis to the three moment CAPM
and provide evidence that the inclusion of this factor leads to superior performance. Ben
Naceur and Chaibi (2007) argue that investors incorporate skewness and kurtosis in the
portfolio decisions in addition to the first two moments.
CRITICISM OF BETA
Beta has not been without criticism. Besides the well known criticism of Fama and
French (1992) that other risk factors should be included in the risk and return relationship,
beta was criticized because historical returns are used as a proxy for expected returns. The
CAPM assumes that the expected return will be the same as the realized return.
A well known critique on the CAPM comes from Roll (1977). Roll argues that the
CAPM may not be testable. The CAPM implies that the market portfolio is mean-variance
efficient. A portfolio is called mean-variance efficient if it has a minimal variance for a
certain amount of return. Roll (1977) criticized the CAPM because the market index was used
as a proxy of the portfolio. He argued that the CAPM cannot test the true market portfolio
since the true market portfolio cannot be measured. According to Roll, the true portfolio must
include all assets, financial, real as well as human and not just stocks. Roll (1977) shows that
15
Hafez Barahmeh 2010
the linearity test is a test of whether the portfolio used in the analysis is mean-variance
efficient, rather than a test of the CAPM. Consequently the CAPM is not testable unless the
true market portfolio composition is known and used in the test. When the proxy of the
market portfolio is mean-variance efficient, the relationship between returns and beta could
turn out to be linear. The opposite is also true, if the proxy is not mean-variance efficient, the
relationship could be non-linear. Regardless of the returns being mean-variance efficient,
most proxies of the market are very highly correlated with the true market portfolio and with
each other according to Roll (1977). Nevertheless, the results differ depending on which
proxy is used for the market.
The CAPM model can be sensitive to the return interval used to test beta, because
beta can vary with the length of the interval used to measure returns. The beta of a company
may change over time. Research has shown that beta is not stable over time. As a
consequence the results of this study are only valid for the time interval used. A time varying
beta could be a solution for this problem.
The CAPM measures risk by the beta of the asset. The upside moments and the
downside moments of returns are treated equally by the CAPM. However, empirical studies
have shown that return distributions typically have fat tails and are not symmetrical
(Galagedera, 2006). Furthermore, the CAPM is a one-period model and does not have a time
dimension. The assumption that returns are normally distributed over time is necessary in
order to estimate the model through time. According to Pereiro (2001) the straight application
of the classical CAPM is controversial when it is not clear whether the hypothesis of market
efficiency holds. Moreover, the CAPM based models are not structure to deal with
unavoidable, unsystematic risk arising from imperfect diversification.
Despite the criticisms of the CAPM it is still used by many professionals. Mainly
because it offers a statistical framework that allows for an analysis of behavior in capital
markets. Omran (2007) points out that inferences can be drawn about the realized returns and
company characteristics like the market risk and investors’ preference for skewed
distributions. These inferences that are made do not constitute tests of the CAPM but the
provides useful insights into capital markets. Hereafter the versions of the CAPM that are
used in this study are described.
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Capital Asset Pricing in Emerging Market: an Empirical Investigation
VERSIONS OF THE CAPITAL ASSET PRICING MODEL
THE TRADITIONAL CAPM
According Graham and Harvey (2001) most practitioners use this version of the
CAPM. The Sharpe (1964) & Lintner (1965) the formula for the expected return in the
traditional version of the CAPM can be written as:

E( Ri )  R f   iM E( Rm )  R f

[1]
Where:
Ri is the return on asset
R f is the risk-free interest rate
Rm is the return on the market portfolio,
 iM 
cov( Ri , RM )
 M2
is the systematic risk of asset i relative to the market portfolio.
The coefficient  iM , is estimated by the following time-series regression of the weekly
average excess returns to asset i , Rit  R ft , against weekly excess market returns, Rmt  R ft .


Rit  R ft   i   iM Rmt  R ft   it
Where  iM 
cov( Ri , RM )
 M2
i = 1,…..n and t = 1,…T
[2]
is the systematic risk of asset i .
EMERGING MARKET CAPM MODELS
The traditional CAPM can be used in various ways, depending on which parameters
are used in the equation. For instance, in some models the risk free rate that is used is the
global risk free rate (the 10-year US Treasury note), in others a local risk free rate is used.
These variations have lead to different models that are differently applicable. Hereafter
several versions of the traditional CAPM model will be described.
THE GLOBAL CAPM
Investors that believe in market integration could apply the Global CAPM, also
known as the World CAPM, to emerging markets (O’brien, 1999; Stulz, 1999). This model is
based on the idea that an investor located anywhere in the world could rapidly enter and leave
17
Hafez Barahmeh 2010
any market, incurring minimum transaction costs (Pereiro, 2001). The global CAPM is in fact
similar to the traditional CAPM, but is denoted here in order to clarify the input parameters.
The expected return in the Global CAPM is defined by the following formula:

M
E( Ri )  R fG   iGlobal
E( RmG )  R fG

Where:
Ri is the return on asset i
R fG is the global risk-free interest rate,
RmG is the return on the global market portfolio and
M
 iGlobal
is the systematic risk of asset i relative to the global market portfolio, in other words,
the local company beta computed against a global market index.
THE THREE MOMENT CAPM
As mentioned before, empirical evidence shows that mean and variance alone are not
sufficient to explain the return distributions. An additional factor is proposed by Kraus and
Lintzenberger (1976). They propose to include the next moment, skewness, into the equation.
Investors prefer portfolios that are skewed to the right to portfolios that are skewed to the left
(Harvey and Siddique, 2000). As a result, higher expected returns should be required for
assets that have more left skewed returns, and vice versa. Some studies have shown that
investors are willing to pay for positive skewness (Omran, 2007). This has led to the three
moment CAPM that includes skewness (see Elton, Gruber, Brown, & Goetzmann, 2003). The
formula for the expected return in the three moment CAPM can be written as:



E ( Ri )  R f  1M E ( Rm )  R f   2M E ( Rm )  R f

2
[3]
Where  1M and  2M are the slopes in the following regression:



Rit  R ft   i  1M Rmt  R ft   2M Rmt  R ft

2
  it
i = 1,…..n and t = 1,…T
[4]
18
Capital Asset Pricing in Emerging Market: an Empirical Investigation
THE FOUR MOMENT CAPM
In the four moment CAPM, the preference of the investor is extended by adding
kurtosis to three moment CAPM (Dittmar, 2002). Kurtosis is intended to capture the
probability of outcomes that are highly divergent from the mean (Ben Naceur and Chaibi,
2007). Kurtosis is described by Darlington (1970) as the degree to which, for a given
variance, a distribution is weighted towards its tails. The formula for the expected return in
the four moment CAPM is defined by the following expression



E ( Ri )  R f  1M E ( Rm )  R f   2M E ( Rm )  R f

2

  3M E ( Rm )  R f

3
[5]
Where  1M ,  2M and  3M are the slopes in the following regression:



Rit  R ft   i  1M Rmt  R ft   3M Rmt  R ft

2

  3M Rmt  R ft

3
  it
[6]
i = 1,…..n and t = 1,…T
THE LOCAL CAPM
When dealing with emerging markets, investors often believe that financial market
integration is constrained. If financial markets are integrated, investors do not have to worry
about country risk, since it is diversified away through a geographically diversified portfolio
(Pereiro, 2001). However, in the case investors are constrained form entering and leaving
specific country markets, they may come to bear country-related risk. Only 5% of respondents
in a survey by Keck et al. (1998) believed world financial markets to be deeply integrated.
The local CAPM defines expected return as follows:
M
E( Ri )  R fL   iLocal
( RmL  R fL )
[7]
Where:
R fL  R fG  RC
R fL
being the local risk-free rate
M
 iLocal
the local company beta against a local market index
RmL the return of the local market
19
Hafez Barahmeh 2010
RC the country risk premium
R fL
is the composite of the global risk-free rate and the country risk premium
RC .
The country risk premium is usually computed as the spread of sovereign bonds over
the global bonds of similar denomination, yield and term e.g. a US Treasury note if the US
market is considered as the global market proxy (Pereiro, 2001). In this study the country risk
premium is computed as the spread between a US 10-year Credit Default Swap (CDS) and a
similar sovereign CDS.
The following models previously discussed will be analyzed using stock market data
of four emerging markets; the Global CAPM, the three moment CAPM, the four moment
CAPM and the local CAPM.
20
Capital Asset Pricing in Emerging Market: an Empirical Investigation
CHAPTER 3. RESEARCH METHODOLOGY
DATA SELECTION
This study uses data collected from the Thomson Financial DataStream database.
Data is collected for 739 companies from five emerging markets for the period January 2001
to September 2009 based on the availability and length of data sets maintained by
DataStream. After analysis of the companies, 516 companies were used and 223 companies
were excluded. The countries that are included are Egypt, Israel, Morocco and Turkey. US
dollar denominated total returns are collected for the companies and the indices of the
emerging markets. This is done in order to maintain uniformity of results. The return data
used in this study are sampled at a weekly frequency. To calculate returns, DataStream
adjusts weekly prices for dividends, splits and share issues. The lengths of the data samples
are not uniform. The data on the returns for Egypt, Israel, Morocco and Turkey covers the
period from January 12, 2001 to September 25, 2009. The dataset comes from the Datastream
database. In order to avoid influence of survivorship bias, non survival shares are included in
the tests as well.
Data for the market indices was also collected for the five emerging market indices
using the DataStream database. The MSCI, S&P and FTSE market indices were collected.
Analysis of the indices showed that the MSCI indices were most useful for inclusion in the
analysis, due to the availability and length of the data. The weekly US dollar denominated
returns on the Morgan Stanley Commodity Index (MSCI) World index are also collected from
the DataStream Database for the period January 12, 2001 to September 11, 2009. The MSCI
World index is used as a proxy for the returns on the world market portfolio.
When investigating the CAPM a proxy for the risk free rate is needed. Researchers
often use the 10 year US Treasury-bill as a proxy for the risk free rate. This data is collected
for each week from the website of the Federal Reserve Bank2 for the period January 12, 2001
to September 25, 2009.
Since no data on the local risk free rate was available, Credit Default Swap (CDS)
data was used. For Egypt, Israel, Morocco and Turkey as well as for the US, daily data on the
mid rate spreads of 10 year Senior Credit Default Swaps were collected for the period April
20, 2006 to September 25, 2009. The country risk premium was calculated as the spread
between the emerging market rate and the US rate. The global financial crisis influenced the
2 www.federalreserve.gov
21
Hafez Barahmeh 2010
country risk premiums, in some cases the risk premiums almost doubled. Therefore the CDS
spreads that are used in the analysis are separately calculated for three periods, the total
period (April 2006 to September 2009), before the onset of the financial crisis (April 2006 to
August 2008) and the period during the financial crisis (September 2008 to September 2009).
To assess the robustness of the results, the sample of the historical returns are divided
into three periods. The first period start January 2001 and ends September 2009 (total period).
The second period starts January 2001 and ends August 2008 (before crisis) and the third
period starts September 2008 and ends September 2009 (during crisis).
STOCK MARKET ACTIVITY
Stocks traded on emerging equity markets are not always as frequently traded as
stocks listed on developed equity markets. In addition, data series on quoting companies can
be unacceptably short. As a result it is necessary to make a distinction between active and
inactive stocks. Omran (2007) applies a methodology to determine whether a stock has been
active or inactive. Two statistics are computed to determine whether a stock is active or
inactive. The first statistic is α, the percentage of weeks the stock has been active to the
number of weeks in the period. A stock is selected when the percentage of weeks the stock
has traded is at least 90%. The second statistics, ω, is the average number of transaction for
each stock during the period. Since the number of transactions is not available, in this article
the companies that have at least 100 observations during the period are selected.
METHODOLOGY
In this part of the article the methodology to test the theoretical models is defined.
Several parameters have to be computed in order to be able to test the versions of the CAPM.
The methods of
constructing the parameters of the models are described. To facilitate
comparison across countries ,the analysis is conducted from the perspective of a US-based
internationally diversified investor.
GLOBAL RISK FREE RATE
The appropriate risk free rate is that at which an investor can tie his money at the
current point in time in that market (Pereiro, 2002). The US is considered by many as the
most efficient market in the world. As a result the US Treasury bill is frequently used as
proxy for the risk free rate. Since there is more than one choice of risk free rate, this study
will use the most frequently used option. This will make comparison with earlier research
more straightforward. A 10-year US Treasury bond is used as a proxy for the risk-free rate,
the rates are converted to weekly frequency. In doing so, investors are expected to price stock
based on anticipated US dollar returns. This approach to the global CAPM does not ignore
currency risk because currency risk is included in the regression (Bodnar et al, 2003). Bodnar
22
Capital Asset Pricing in Emerging Market: an Empirical Investigation
et al. (2003) suggest that currency risk should be included in the global CAPM as a separate
factor. However, this is not done in this study. Koedijk et al. (2002) show that les than 4% of
a stocks total variance are explained by currencies.
There is no agreement among academics on the true historical value of the US riskfree rate. Pereiro (2001) finds a median return for 30-year T-bonds for the period 1993-1999
of 6.72%. The median return for the 10-year US Treasury bond found in this article is 6% for
the period 1987-2009, when looking at the period 2001-2009 an average of 8.3% is found.
COUNTRY RISK PREMIUM
In the middle eastern economies, computing the market risk premium is a difficult
endeavor, given the high volatility of the financial environment. The premiums may differ
according to the time period considered. Damodaram (2000) suggests that the market
premium of an emerging economy may be considered the premium in a developed market
with a country risk premium added to it. Normally there is a high correlation between market
risk and sovereign risk. Following this line of thought, the country risk premium is computed
as the spread between a US Credit Default Swap (CDS) and a similar sovereign CDS. The
CDS data was collected from Thomson Financial DataStream for the US, Egypt, Israel,
Morocco and Turkey. The mid rate spreads between the entity and the relevant benchmark
curves were collected for 1 -10 year senior Credit Default Swaps. The rates were expressed in
basis points.
Usually this parameter is computed as the spread between a global bond and a similar
sovereign bond, however this data was not available. Moreover, choosing a specific sovereign
bond is a difficult endeavor, since issues vary a lot in terms of technical features. The CDS
data is easier to compare since they are all senior CDS and were matched on maturity.
LOCAL RISK FREE RATE
The local risk-free rate is obtained by adding the country risk premium (the mid rate
spreads between the US and the countries’ 10 year senior Credit Default Swaps) to the global
risk-free rate (10-year US Treasury bond).
MARKET PORTFOLIO
The MSCI World index is used as a proxy for the global market portfolio. The returns
on the market index in each country are used as a proxy for the returns on the market portfolio
in this country. The indices used were the MSCI Egypt, MSCI Israel, MSCI Morocco and
MSCI Turkey.
23
Hafez Barahmeh 2010
BETA ESTIMATION
Following the methodology used by Fama & Macbeth (1973), historical returns of the
individual stocks are calculated following equation [2] and beta is estimated following
equation [3] using the Ordinary Least Squares (OLS) regression method. For each company
in the sample the returns are regressed on the stock index to estimate beta. A similar
methodology will be used to estimate the different versions of the CAPM.
The first stage is the time series regression of individual stock returns on the proxy for
the market index. The second stage is a cross section regression of average returns for each
security on each security’s beta, skewness, and kurtosis. The regression residuals are checked
for heteroscedasticity and normality (Omran 2007).
In the first stage regression, time series data is used to estimate market risk. The second stage
regression is a cross sectional regression. Afterwards two diagnostics test are applied to the
residuals from the regression; the White Heteroscedasticity test, since cross sectional data
could suffer from the variance of the error term being large for large beta stocks and small for
small beta stocks. The second diagnostics test is testing for normality of the residuals using
Jarque-Bera test for normality, skewness and excess kurtosis. R 2 is the ratio of market risk
to the total variance of the returns on a particular stock. The percentage of variation not
explained by the market is 1  R 2 .
Beta estimation has some problematic issues. The value of beta depends on the length
of the time series taken, taking a 2 year or a 5 year time interval will result in different
measures of beta. As a result there exists more than one beta for the same company (Pereiro,
2001). In addition beta changes over time. The beta of a company tends to shrink as the
company matures. It is difficult to account for these changes.
24
Capital Asset Pricing in Emerging Market: an Empirical Investigation
EMPIRICAL RESULTS AND INTERPRETATION OF THE
FINDINGS
The main question in this article is whether the CAPM is applicable in emerging
markets and if so, which model performs best in terms of the amount of variance explained by
the model. In order to test the CAPM, the returns of individual companies of four emerging
markets were investigated, namely Egypt, Israel, Morocco and Turkey. Besides the traditional
CAPM (Global CAPM), three other versions of the model were investigated; the Three
Moment CAPM, the Four Moment CAPM and the Local CAPM. In addition, the models were
tested in three different time periods (total period, before the financial crisis, during the
financial crisis). This chapter is organized as follows. First, the properties of the data of the
four emerging markets and the world market will be described. Hereafter, the results of the
regressions of the versions of the CAPM will be described.
EMERGING MARKET DATA
In this section some information about the financial markets used in this analysis will
be discussed. Not much research exists about the financial markets used in this study within
the context of the CAPM. Cheng et al. (2009) stated that besides investigation of Israel during
the hyperinflation in the 1980s, very few researcher have studied the Middle East and North
African countries. Since these financial markets are all relatively new, this does not come as a
surprise. Many of the companies in these countries have done well. For instance, many
companies is Israel are world leader in high-tech sectors (Cheng et al, 2009). The returns in
these countries have been realized while the area experienced major political and security
instability.
The Egyptian stock market (Cairo and Alexandria Stock Exchange, CASE) is the
second largest in Africa. Since the 1980s the stock market started to open up the market to
local and foreign investment, this led to a fast growth in participation in the stock market
(Omran 2007). The Egyptian stock market is open to foreign investments, however there are a
few exceptions, certain companies do not allow foreign stakeholders. According to Omran
(2007) the Egyptian stock market has two major characteristics. First, the stock market is
illiquid and dominated by a small number of stocks. Second, historical data is not available
for long period of time. Omran (2007) states that the Egyptian stock market suffers from thin
trading. The Israeli stock market (Tel Aviv Stock Exchange) is completely open to foreign
investment and plays a major role in the Israeli economy. The Moroccan stock market
(Casablanca Stock Exchange) is the third largest stock market in Africa. The Turkish stock
25
Hafez Barahmeh 2010
market (Istanbul Stock Exchange) is completely open to foreign investment from August
1989.
Hoyer-Ellefsen (2004) composed an ‘information and market efficiency’ score by
combining four trading characteristics. A value of 1 was assigned to each measure that
suggested that the market would behave efficiently. The Egyptian stock market was rated
most efficient with a score of 2, followed by Israel (1), Morocco and Turkey both scored 0,
which indicates that these markets are not efficient.
INVESTIGATION OF THE DATA
The first part of the empirical investigation required an investigation of the available
data. A summary of the descriptive statistics of the stock market data is available in table 1.
The return data on all companies that were taken into account in this study are sampled at a
weekly frequency. The returns were adjusted for dividends, splits and share issues. The
lognormal returns were calculated using the following formula:
Rit  ln( Rit / Rit 1 ) *100
[8]
When dealing with emerging stock markets it is necessary to determine whether a
stock is active or inactive. Since emerging stock markets are often illiquid and dominated by
small number of stocks.
Only stocks that are actively traded and regularly traded are considered for inclusion
in the analysis. In order to correct for thin trading only stocks that traded at least 90% (α =
0.90) of the weeks in the period available and had at least 100 observations were included in
the analysis, except for the period during the financial crisis where all stocks had 56
observations. Furthermore, to filter the data for errors all returns that exceeded a weekly
return of 100% were removed from the sample. Table 1 shows the number of firms that were
included in the analysis. Table 1 also shows that the historical average returns for the four
emerging countries are positive during the total period, however returns are negative during
the financial crisis. As expected the standard deviations of these countries are high and
increase in the period from August 2008 to September 2009. The Egyptian companies display
the highest average weekly returns during the entire period (0.417%), and even during the
period of the financial crisis returns remain positive (0.179%). However, the high returns are
accompanied by high risk. Zhang and Wihlborg (2004) find a monthly average return for
Turkey of 6.6% (sd = 19.299%).
26
Capital Asset Pricing in Emerging Market: an Empirical Investigation
Table 1. Summary statistics of individual company data
Country
No. of
firms
No. firms in
analysis
Weekly Mean *
(SD)
Weekly Mean **
(SD)
Weekly Mean***
(SD)
Egypt
118
79
.417 (7.41)
.451 (6.86)
.179 (10.52)
Israel
256
135
.085 (6.80)
.115 (6.08)
-.121(10.58)
8
38
.293 (4.80)
.358 (4.71)
-.171 (5.35)
357
264
.201 (8.83)
.254 (8.50)
-.174 (10.88)
516
-
-
-
Morocco
Turkey
Total
739
*
Jan
2001 – Sept
2009 (Total period, 455 observations)
**
Jan 5th 2001 – Aug 29th 2008 (Before crisis, 399 observations)
***
Aug 29th 2008 – Sept 25th 2009 (During crisis, 56 observations)
Source : Thomson Financial DataStream (all returns are adjusted for divididend payouts)
5th
25th
Table 2 shows the average weekly returns for the MSCI indices of the emerging
markets as well as for the world market. These indices are used as proxies for the market
indices. The risk-free rate, a 10- year US treasury bill is also displayed. The average returns of
the individual companies included in the analysis differ from the returns on the MSCI country
indices. However the main views remain the same, weekly returns are highest for the
Egyptian stock market (0.49%), this would result in an annual return of over 25%. Weekly
returns are lowest for Israel’s stock market (0.10%) for the total period, this would result in an
annual return of approximately 5%. As was the case for the individual company returns, the
standard deviation is highest for Turkey (6.99) and lowest for Morocco (2.81) for the total
period. The proxy for the world market, the MSCI World displays positive weekly returns
during the period before the financial crisis (0.07%) but becomes negative during the crisis (0.27%), as expected the standard deviation of the world market is lower than the standard
deviation of the individual countries. Table 2 also shows measures of skewness and kurtosis.
Skewness is a measure of symmetry of the return distribution. A positive value means that the
distribution is skewed to the right, while a negative value indicates that the distribution is
skewed to the left. Kurtosis is a measure of peakedness of a distribution. Kurtosis measures
the shape of a distribution relative to a normal distribution, the typical value of kurtosis is
three. The Jarque-Bera statistic combines these two measures to determine whether a
distribution is normally distributed. Table 2 shows that the return distributions for the total
period are skewed to the left, thus negative. In line with this finding, Bekaert, Erb, Harvey
and Viskanta (1998) find that over the period 1987-1997 the MSCI indices display negative
skewness, however the IFC indices display positive skewness during this period. It must be
noted that the assumption of normality of the return distributions does not hold for the
27
Hafez Barahmeh 2010
markets used in this study. For all countries and periods, except Israel during the financial
crisis, the assumption of normally distributed returns must be rejected. This finding is in line
with earlier research on the return distributions of emerging markets. Bekaert et al. (1998)
investigate skewness and kurtosis and find evidence that the distribution of emerging market
returns are not-normal. Interestingly table 2 shows that the returns of the markets are closer to
being normally distributed during a period of financial crisis, the Jarque-Bera measures are
much smaller and for some markets the assumption of normality cannot be clearly rejected.
Table 2. Descriptive statistics for the market returns and the risk-free rate series in four emerging
markets
Jan 5th 2001 – Sept 25th 2009 (Total period)
Index
Mean
SD
Min.
Max.
Skewness
Kurtosis
JB (prob.)
MSCI Egypt
.49
4.38
-21.92
12.32
-.91
6.58
306.59 (.000)*
MSCI Israel
.10
3.18
-12.98
11.79
-.44
4.47
55.66 (.000)*
MSCI Morocco
.30
2.81
-14.28
8.47
-.66
5.68
170.16 (.000)*
MSCI Turkey
0.26
6.99
-27.36
35.7
-0.01
6.24
197.57 (.000)*
MSCI World
.03
2.725
-22.33
11.70
-1.41
14.36
2597.85 (.000)*
Riskfree rate (weekly)
.08
.01
.04
0.11
-.65
3.53
37.07 (.000)*
Jan 5 2001 – Aug 29 2008 (Before crisis)
th
th
MSCI Egypt
.59
3.77
-15.76
12.32
-.17
4.38
33.31 (.000)*
MSCI Israel
.12
3.11
-12.98
11.79
-.36
4.67
55.02 (.000)*
MSCI Morocco
.40
2.54
-9.75
8.47
-.34
4.80
61.72 (.000)*
MSCI Turkey
0.3
6.66
-23.72
35.7
0.06
6.45
196.96 (.000)*
MSCI World
.07
2.04
-9.94
7.88
-.51
5.05
87.12 (.000)*
Riskfree rate (weekly)
0.09
.01
.06
.11
-.02
2.31
7.86 (.020)**
Aug 29 2008 – Sept 25 2009 (During crisis)
th
th
MSCI Egypt
-.25
7.40
-21.92
10.77
-1.18
4.07
15.56 (.000)*
MSCI Israel
-.02
3.63
-10.06
6.93
-.74
3.38
5.45 (.065)
MSCI Morocco
-.44
4.24
-14.28
7.45
-.74
4.02
7.55 (.023)**
MSCI Turkey
-0.06
9.1
-27.36
25.01
-0.19
4.63
6.50 (.038)**
MSCI World
-.27
5.58
-22.33
11.70
-1.04
5.94
30.27 (.000)*
Riskfree rate (weekly)
.06
.01
.04
.08
-.46
2.11
3.83 (.147)
Returns are weekly returns in percentage
*
JB significant at a 1% level
**
JB significant at a 5% level
Source MSCI indices: Thomson Financial Datastream Database
Source for the risk-free rate: website of the Federal Reserve Bank
28
Capital Asset Pricing in Emerging Market: an Empirical Investigation
MARKET CORRELATIONS
Table 3 displays the correlations of the MSCI country indices and the MSCI World
index during different time periods. As the table shows the correlations between the emerging
markets are low. Furthermore, in the periods before the financial crisis the countries are not
highly correlated with the world market, as expected Israel (0.547) and Turkey (0.380) show
the highest correlation with the world market. The table shows that during normal economic
conditions the returns on these five markets are not highly correlated with each other, nor
with the world market. However, in times of financial crisis stock market tend to become
more correlated, Baig and Goldfajn (1998) find evidence of contagion in Asian financial
markets during the Asian Crisis. Cross-country correlations increased significantly during the
Asian crisis period, this is in line with the findings in table 3. In the period from August 2008
and September 2009 the correlations between the countries increase considerably, for instance
the correlation between Egypt and Turkey changes from 0.110 to .522. The correlation
between the countries and the world market also increase noticeably. Turkey’s correlation
with the world market changes from 0.380 to 0.827, while Egypt’s correlation increases from
0.089 to 0.558. Interestingly Israel’s correlation with the other emerging markets increases in
the crisis period, but the correlation with the world market decreases (0.547 vs. 0.497).
29
Hafez Barahmeh 2010
Table 3. Historical correlations matrix of the four emerging stock markets and the world market
Jan 5th 2001 – Sept 25th 2009
MSCI
MSCI
Egypt
Israel
Egypt
1
Israel
.249
1
Morocco
.241
.134
1
Turkey
.220
.341
World
.288
.493
Jan 5th 2001 – Aug 29th 2008
Morocco
Turkey
World
.196
1
.
.240
.506
1
Turkey
World
MSCI
MSCI
Egypt
Israel
Egypt
1
Israel
.184
1
Morocco
.162
.119
1
Turkey
.110
.303
.124
1
World
.089
.547
.125
.380
1
Turkey
World
Aug 29th 2008 – Sept 25th 2009
Morocco
MSCI
MSCI
Egypt
Israel
Morocco
Egypt
1
Israel
.456
1
Morocco
.399
.188
1
Turkey
.522
.512
.427
1
World
.558
.497
.430
.827
1
COUNTRY RISK PREMIUM
A country risk premium was computed as the spread between a 10-year US Credit
Default Swap (CDS) and a similar sovereign CDS. As table 4 shows, the countries differ in
their market risk premiums. As could be expected the US 10 year senior CDS is very low.
And as Damodaram (2000) suggested it seems that the market premium of an emerging
market can be considered as the premium in the developed market with a country risk
premium added to it. Table 1 shows that Turkey and Egypt have the highest country risk
premiums, whereas Israel and Morocco display a much smaller country risk premium. The
table also shows that the CDS-spread is different according to which time period is
considered. When the CDS-spread before the crisis is compared with the CDS-spread during
the crisis, it shows that the CDS-spread is much larger during the financial crisis, for instance,
Israel’s CDS-spread is more than three times as large (41 vs. 126). Turkey’s CDS-spread just
slightly increases (198 vs. 216), but it must be noted that the value for the CDS-spread was
high before the onset of the financial crisis.
30
Capital Asset Pricing in Emerging Market: an Empirical Investigation
Table 4. Credit Default Swap (CDS) data
Average 10-year
CDS
Average 10-year sovereign CDS – 10-year US CDS
Total period
June 19th 2006 – Sept
25th 2009
Total period
June 19th 2006 –
Sept 25th 2009
Before crisis
June 19th 2006 –
Aug 29th 2008
During crisis
Sept 1st 2008 – Sept
25th 2009
US
25
-
-
-
Egypt
264
239
147
398
Israel
94
69
41
126
Morocco
105
81
54
135
Turkey
223
198
189
216
Country
Source: Thomson Financial DataStream (rates are expressed in basispoints)
EXCESS MARKET RETURNS
Table 5 shows the average excess market returns for the emerging markets, the excess
returns are the company returns minus the risk-free rate. The excess returns, the returns minus
the risk-free rate added by the country risk premium are also displayed. Table 5 shows that
there is a positive risk return tradeoff. According to the CAPM, on average, the realized
market excess returns should be positive although in some periods they can be negative. The
table shows that the excess returns are positive when only the risk-free rate is considered for
the total period and period before the financial crisis. Interestingly, Egypt has a positive
excess return during the crisis. When the country risk premium is taken into account the
positive excess returns become negative. The only country with positive excess returns when
the country risk premium is considered is Morocco, except for the period during the crisis.
Appendix E shows the graphical depictions of the excess returns of the four emerging
markets.
31
Hafez Barahmeh 2010
Table 5: Excess market returns of four emerging markets
Jan 5th 2001 – Sept 25th
2009 (Total period)
Country
Ri  R f Ri  ( R f  Rc )
Jan 5th 2001 – Aug 29th 2008
(Before crisis)
Aug 29th 2008 – Sept 25th
2009 (During crisis)
Ri  R f
Ri  ( R f  Rc )
Ri  R f
Ri  ( R f  Rc )
Egypt
.33
-.23
.36
.02
.12
-.80
Israel
.00
-.16
.03
-.07
-.18
-.47
Morocco
.21
.02
.27
.15
-.23
-.54
Turkey
.12
-.34
.17
-.27
-.24
-.74
Note:
Rf
is the 10-year US treasury bill
Rc is the spread between a 10-year US Credit Default Swap (CDS) and a similar sovereign CDS
BETA ESTIMATES
The second part of the methodology required using OLS for the estimation of betas
for the individual stocks using the historical weekly returns. Following the methodology used
by Fama & Macbeth (1973), historical returns of the individual stocks were calculated and
beta was estimated by using the OLS regression method. Betas were estimated using the
statistical software Eviews 6.0 for individual stocks for the emerging countries. For each
company the returns in the sample are regressed on the proxy for the market, depending on
the version of the CAPM used. Four versions of the CAPM were used and three time periods
were considered. The estimated beta’s can be found in appendices F to I.
The CAPM has a few testable implications. The first proposition is that the
relationship between risk and expected return is linear. In order to test the linearity of the
relationship between risk and expected return, the assumption of a perfect capital market has
to be made, implying that no information or transaction costs are incurred by investors.
Shanken (1995) mentions that the idea of testing the CAPM through the assumption of a
perfect market lacks applicability. The second proposition is that beta is the complete measure
of risk. This can be tested by adding additional variables to the regressions, if other variables
besides beta have explanatory power this is evidence against the CAPM. Lastly, if it is
assumed that investors are risk averse, higher risk should be associated with higher returns.
The estimated betas can be found in appendices F to I. The ranges of the estimated
betas can be found in Table 6. The table shows that, when considering the Global CAPM, the
highest beta value is found in Turkey, and the lowest beta value is found in Morocco. All beta
values estimated using the Global CAPM and the Local CAPM are positive, which indicates a
32
Capital Asset Pricing in Emerging Market: an Empirical Investigation
positive risk return relationship. However when considering the Local CAPM, the highest
value can be found in Israel and the lowest value in Egypt. Interestingly, the Global CAPM
betas of Morocco have a maximum value of 0.360 which is exceptionally low. When
estimating betas according to the local CAPM values become more typical. The Three
Moment CAPM and the Four Moment CAPM sometimes lead to negative beta values.
Table 6: Minimum and Maximum values of the estimated betas
Global
Local
M
 Global
M
 Local
Three Moment CAPM
Four Moment CAPM
 1M
 1M
 2M
 2M
 3M
Egypt Max.
1.246
1.149
1.243
0.015
1.210
0.066
0.006
Egypt Min.
0.017
0.182
-0.047
-0.061
-0.103
-0.042
-0.002
Egypt Average
0.52
.62
.44
-.02
.39
.006
.001
(0.251)
(.256)
(.243)
(.015)
(.241)
(.019)
(.001)
Israel Max.
1.619
1.566
1.673
0.057
1.786
0.047
0.001
Israel Min.
0.177
0.249
0.135
-0.043
0.222
-0.171
-0.008
(SD)
Israel Average
.79
.71
.85
.01
.92
-.01
.00
(SD)
(.288)
(.221)
(.319)
(.018)
(.337)
(.028)
(.002)
Morocco Max.
0.360
1.157
0.373
0.009
0.366
0.019
0.001
Morocco Min.
0.051
0.285
0.001
-0.030
0.0071
-0.067
-0.003
Morocco Average
.21
.71
.16
-.01
.20
-.03
.00
(SD)
(.082)
(.226)
(.086)
(.010)
(.090)
(.017)
(.000)
Turkey Max.
1.869
1.191
1.659
0.022
1.659
0.060
0.006
Turkey Min.
0.538
0.485
0.501
-0.078
0.392
-0.148
-0.007
Turkey Average
(SD)
1.14
.81
1.05
-.02
1.03
-.01
.00
(.213)
(.136)
(.209)
(.018)
(.218)
(.228)
(.002)
Note: Number of cross-sections; Egypt (79), Israel (135), Morocco (38), Turkey (264).
Table 7 shows the result of the cross-sectional regression of the Global CAPM.
 0 is
the expected excess return on a zero beta portfolio.  1 is the market price of risk (the
difference between the expected rate of return on the market and a zero beta portfolio). The
linear relationship between the dependent variable (return) and the explanatory variable (beta)
is examined. In the case of Egypt, the constant variable
 0 is significantly different from
zero in all three periods, therefore based on the intercept criterion the CAPM hypothesis can
be rejected. The beta coefficients  1 are significant, which means that beta does have an
explanatory power on return for all periods. It can be seen from the F-test that the model as a
2
whole is statistically significant. However, the adjusted R value shows that the goodness of
fit of the explanatory variable is very modest. If the residuals are normally distributed, the
33
Hafez Barahmeh 2010
hypothesis of normaltity of the Jarque-Bera statistic must not be rejected. Table 7 shows that
the null hypothesis of a normal distribution can be rejected for the total period and the period
during the crisis. A heteroscedasticity test was conducted, since cross sectional data could
suffer from the variance of the error tem being large for large beta stocks and small for small
beta stock (Omran , 2007). No signs of heteroscedasticity were found. The Durbin Watson
(DW) statistic showed no signs of autocorrelation, so OLS is justified.
When the results of the Global CAPM of Israel are investigated, it shows that the
constant variable
 0 is only significantly different from zero when the period before the
crisis is considered. During the crisis and for the total period the CAPM the intercept criterion
cannot be rejected. The beta coefficients  1 are significant, except when the total period is
considered. It can be seen from the F-test that the model as a whole is statistically significant,
2
except when the total period is considered. However, the adjusted R value shows that the
goodness of fit of the explanatory variables are again very modest. As for Egypt and Israel,
the results obtained for Morocco and Turkey are mixed. Some periods show evidence
supporting the CAPM, for instance Turkey during the crisis, where the constant variable
0
is not significantly different from zero, therefore based on the intercept criterion the CAPM
hypothesis cannot be rejected. The beta coefficient  1 is significant on a 5% level, which
means that beta does have an explanatory power on return for all periods. Furthermore, it can
be seen from the F-test that the model as a whole is statistically significant. However, the
2
adjusted R value shows that the goodness of fit of the explanatory variable is once again
very modest. Evidence against the CAPM comes from the period before the crisis for both
Morocco and Turkey. In both cases the
 0 is significantly different from zero and the beta
coefficients  1 are insignificant.
34
Capital Asset Pricing in Emerging Market: an Empirical Investigation
Table 7: Global CAPM
Global CAPM
M
E( Ri )  R f   0   1 * Global
 i
Egypt
0
1
.20*
.24*
(3.79)
(2.66)
.27*
.38*
Before
(7.62)
(3.16)
.66*
-.66*
Crisis
(3.79
(-3.41)
Period
Total period
Adj.R 2
Fvalue
JB ( prob )
21.59*
.07
.07
(.000)
2.23
.10
.10
(.32)
.12
.12
(.000)
Fvalue
JB ( prob )
74.41*
Israel
Total period
Before
Crisis
0
1
.03
-.03
(.38)
(-.38)
.21*
-.22*
(2.91)
(-2.66)
.15
-0.41*
(1.04)
(-2.65)
Adj.R 2
14.13*
.00
.00
(.000)
18.85*
.04
.04
(.000)
36.53*
.04
.04
(.000)
Fvalue
JB ( prob )
Morocco
0
1
.32*
-.52
Total period
(3.97)
(-1.45)
.29*
-.17
Before
(5.78)
(-.57)
.02
-.88
(.11)
(-1.36)
Crisis
Adj.R 2
1.53
.03
.03
(.464)
-.02
-.02
(.996)
.01**
.45
.02
.02
(.798)
Adj.R 2
Fvalue
JB ( prob )
.00
.00
(.195)
Turkey
1
0
Total period
Before
Crisis
Note:
*
**
***
.11
.00
(1.35)
(.06)
.25*
-.09
(3.41)
(-1.19)
.14
-.30**
(.85)
(-2.34)
3.27
2.64
.00
.00
(.267)
39.86*
.02
.02
(.000)
Significant at a 1% level
Significant at a 5% level
Significant at a 10% level
35
Hafez Barahmeh 2010
Table 8 shows the result for the Three Moment CAPM. Again the results are mixed. For some
countries and periods the constant variable
 0 is not significantly different from zero,
therefore based on the intercept criterion the CAPM hypothesis cannot be rejected. However
in other periods the constant variable
 0 is significantly different from zero. The beta
coefficient  1 is significant on a 1% or 5% level mainly in Egypt and Israel, which means
that beta does have an explanatory power on return for these periods. However, in Morocco
the beta coefficients  1 are insignificant. The CAPM states that no other measure than beta
can explain risk, based on the results for the Three Moment CAPM this hypothesis can be
rejected in the case of Israel and Turkey during the crisis, where  2 is significant on a 1% or
10% level. However, for all other countries and period  2 is not significant and does not have
explanatory power, which is in favor of the CAPM. In addition, it can be seen from the F-test
that the models are not statistically significant in most cases. Once more, the adjusted R
2
values show that the goodness of fit of the explanatory variables are very modest.
36
Capital Asset Pricing in Emerging Market: an Empirical Investigation
Table 8: Three Moment CAPM
Three Moment CAPM
E( Ri )  R f   0   1 * 1M   2 * 2M   i
Period
Total period
Before
Crisis
0
1
Egypt
2
.19*
.24**
-1.22
(3.44)
(2.49)
(-.77)
.273*
.39*
-.46
(6.93)
(3.12)
(-.70)
.62*
-.67*
.77
(3.47)
(-3.43)
(.18)
Adj.R 2
Fvalue JB ( prob )
21.37*
.06
(.04)
.09
(.00)
(.000)
2.13
(.344)
65.94*
.12
(.00)
(.000)
Israel
1
2
.05
-.12
4.47*
(.70)
(-1.40)
(3.00)
.19*
-.19**
.96***
(2.74)
(-2.29)
(1.72)
.19
-.56*
7.47***
(1.33)
(-2.89)
(1.91)
0
1
.33*
-.49
3.36
(3.87)
(-1.35)
(1.07)
.29*
-.19
-.09
(5.20)
(-.61)
(-.06)
.10
-.96
12.87
(.47)
(-1.47)
(1.59)
0
Total period
Before
Crisis
Adj.R 2
Fvalue JB ( prob )
9.41*
.05
(.01)
(.009)
18.04*
.05
(.01)
.06
(.02)
(.000)
33.18*
(.000)
Morocco
Total period
Before
Crisis
2
Adj.R
2
Fvalue JB ( prob )
.97
.01
(.33)
(.617)
.00
-.05
(.83)
(.998)
2.62
.02
(.25)
(.269)
Turkey
1
2
.11
-.02
-1.31
(1.39)
(-.32)
(-1.57)
.25*
-.09
-.05
(3.31)
(-1.24)
(-.17)
.13
-.27**
3.31***
(.76)
(-2.03)
(1.77)
0
Total period
Before
Crisis
Note:
*
**
***
Adj.R
2
Fvalue JB ( prob )
3.88
.00
(.26)
(.143)
2.70
-.00
(.45)
(.260)
44.24*
.02
(.05)
(.000)
Significant at a 1% level
Significant at a 5% level
Significant at a 10% level
37
Hafez Barahmeh 2010
Table 9 shows the results of the cross-sectional regression for the Four Moment
CAPM. Table 9 shows that for some countries (Israel) and periods (during the crisis, except
for Egypt) the constant variable
 0 is not significantly different from zero, therefore based
on the intercept criterion the CAPM hypothesis cannot be rejected. However in other
countries (Egypt) and periods (before the crisis, except for Israel) the constant variable
 0 is
significantly different from zero. The beta coefficient  1 is significant on a 1% or 5% level
only in Egypt, which means that beta does have an explanatory power on return for these
periods. Nonetheless, in the other countries the beta coefficients  1 are insignificant, this is
evidence against the CAPM. The CAPM states that no other measure than beta can explain
risk, based on the results of the Four Moment CAPM this hypothesis can be rejected in the
case of Israel for all periods and for Turkey in the total period, where  2 is significant on a
1% , 5% or 10% level. However,  2 is not significant and does not have explanatory power
in the other countries and periods, which is in support of the CAPM. As the CAPM expects
no other variable to explain risk besides beta, the coefficient  3 is not expected to be
significant. This hypothesis of the CAPM cannot be rejected for most countries and periods,
which is in support of the CAPM. Interestingly, in two cases the coefficient is significant at a
5% level and has explanatory power, in Egypt before the crisis and in Turkey during the
crisis. The F-test shows that the models are not statistically significant in Morocco and
Turkey, except Turkey during the crisis. Interestingly, in Morocco none of the explanatory
variables are significant. The F-values in Egypt and Israel are mostly significant. Once more,
2
the adjusted R values shows that the goodness of fit of the explanatory variable are modest,
but the values of Egypt and Israel have increased..
38
Capital Asset Pricing in Emerging Market: an Empirical Investigation
Table 9: Four Moment CAPM
Four Moment CAPM
E ( Ri )  R f   0   1 * 1M   2 *  2M   3 *  3M   i
Egypt
0
1
2
.19*
.23**
-1.00
33.61
Total period
(3.41)
(2.40)
(-.59)
(1.30)
.28*
.36*
-.49
8.81**
Before
(6.23)
(2.79)
(-.69)
(2.20)
.67*
-.69*
2.55
-4.89
Crisis
(3.81)
(-3.62)
(.59)
(-.06)
Period
3
Israel
0
1
2
.08
-.09
4.43*
-27.44
Total period
(1.31)
(-1.11)
(3.07)
(-.98)
-.06
.06
2.44***
-24.60
Before
(-1.10)
(.87)
(1.77)
(-.84)
.20
-.52*
7.37***
-135.55
(1.41)
(-2.63)
(1.88)
(-1.63)
Crisis
0
1
3
Morocco
2
3
.32*
-.48
3.41
-85.24
Total period
(3.36)
(-1.30)
(1.07)
(-1.30)
.27*
-.15
-.17
-10.26
Before
(4.61)
(-.47)
(-.12)
(-1.15)
.31
-1.09
13.12
-195.89
(1.08)
(-1.65)
(1.63)
(-1.06)
Crisis
0
Total period
Before
Crisis
Note:
*
**
***
Turkey
1
2
3
.09
-.01
-1.84**
12.78
(.24)
(-.17)
(-1.98)
(.92)
.20**
-.07
-.18
-1.76
(2.53)
(-.94)
(-.54)
(-.88)
.12
-.24***
2.47
-88.65**
(1.27)
(-2.58)
(.73)
(-1.84)
Significant at a 1% level
Significant at a 5% level
Significant at a 10% level
Adj.R 2 Fvalue
JB ( prob )
21.95*
.05
(.078)***
(.000)
.08
(.024)**
(.462)
.16
(.001)*
1.54*
94.74*
Adj.R 2 Fvalue
(.000)
JB ( prob )
13.30*
.11
(.000)*
.08
(.002)*
(.001)
16.47*
(.000)
19.10*
.05
(.028)**
Adj.R 2 Fvalue
(.000)
JB ( prob )
.97
-.02
(.511)
(.616)
-.03
(.610)
(.963)
.08
1.67
.03
(.259)
Adj.R 2 Fvalue
(.433)
JB ( prob )
3.37
.01
(.222)
(.185)
2.22
.00
(.602)
(.329)
30.59*
.02
(.039)**
(.000)
Table 10 shows the results of the cross-sectional regression of the Local CAPM.
39
Hafez Barahmeh 2010
When the result of the Local CAPM are investigated, it shows that the constant
variable
 0 is only significantly different from zero at a 5% level when the total period of
Egypt and Turkey before the crisis are considered. For all other countries and periods, the
CAPM intercept criterion cannot be rejected, which is in favor of the CAPM. The beta
coefficients  1 are significant in all periods during the crisis, which is in support of the
CAPM. However, when the total period and the period before the crisis of Egypt and Israel
are considered beta coefficients do not reach significance. In addition these beta values are
negative is many cases, which implies a negative risk return relationship. According to the
CAPM, higher risk should be associated with higher returns, the results of this study do not
support thos hypothesis. The F-tests show that the model is significant when beta is
2
statistically significant. The R values shows that the goodness of fit of the explanatory
variables are again very modest. The most interesting finding is that the Local CAPM seems
2
to work particularly well during times of crisis, where the R values are higher and the
CAPM cannot be rejected. The Local CAPM was also investigated without considering the
country risk premium, in this case it shows that the constant variable
 0 is significantly
different from zero in many cases. Based on these results the intercept criterion of the CAPM
can be rejected. The overall result of the different versions show mixed evidence. Both in
favor as well as against the CAPM. Furthermore, the explanatory power of the models is very
modest
40
Capital Asset Pricing in Emerging Market: an Empirical Investigation
Table 10: Local CAPM
Local CAPM
M
E( Ri )  ( R f  Rc )   0   1 *  Local
 i
Egypt
0
1
-.29*
.11
Total period
(-4.65)
(1.16)
-.04
.09
Before
(-.58)
(.92)
-.28
-.66*
(1.60)
(-3.19)
Period
Crisis
Adj.R 2
Fvalue JB ( prob )
22.16*
.00
(.248)
.00
(.361)
(.000)
5.32***
(.070)
68.33*
.11
(.002)*
(.000)
Israel
0
1
.02
-.25**
(.26)
(-2.46)
.11
-.28*
Before
(1.58)
(-2.67)
-.00
-.43*
Crisis
(-.02)
(-3.28)
Total period
Adj.R 2
Fvalue JB ( prob )
15.50*
.04
(.015)**
(.000)
31.22*
.04
(.008)*
.07
(.001)*
(.000)
23.57*
(.000)
Morocco
Total period
Before
Crisis
0
1
.04
-.02
(.36)
(-.13)
.10
.07
(.94)
(.47)
.04
-.88*
(.19)
(-3.03)
Adj.R 2
Fvalue JB ( prob )
1.70
-.03
(.898)
(.427)
.06
-.02
(.639)
(.969)
6.02**
.18
(.005)*
(.049)
Turkey
Total period
Before
Crisis
Note:
*
**
0
1
.17***
.21***
(-1.88)
(-1.87)
-.05
-.28**
(-.46)
(-2.32)
-.36**
-.43**
(-2.20)
(-2.33)
Adj.R 2
.01
Fvalue JB ( prob )
(.063)**
*
3.94
(.140)
3.24
.02
(.021)**
(.198)
34.76*
.02
(.021)**
(.000)
Significant at a 1% level
Significant at a 5% level
41
Hafez Barahmeh 2010
***
Significant at a 10% level
CONCLUSION
This paper has studied the relatively understudied financial markets of Egypt, Israel,
Morocco and Turkey within the context of the Capital Asset Pricing Model. The countries
analyzed in this study are emerging markets which have different characteristic compared to
established financial markets.
The weekly stock returns were investigated by testing different versions of the
CAPM, namely the Global CAPM, the Three Moment CAPM, the Four Moment CAPM and
the local CAPM. Moreover, the models were tested under differing financial conditions. The
study used weekly return from 516 companies listed on the CASE, ISE, TASE, CSE from
January 2001 to September 2009.
The main question in this article was whether the CAPM is applicable in emerging
markets, and if so, which model performs best. The CAPM was expected to be applicable in
emerging markets and a linear relationship between the expected return on a security and its
risk was expected. The betas values that were found show mixed evidence. Some values were
positive which indicates that there exists a positive risk return relationship in the emerging
markets. However, negative beta values were also found. This would indicate that higher risk
is associated with lower returns. In addition, higher risk was expected to be associated with
higher expected return and risk aversion. The results do not support the hypothesis that there
exists a positive risk return relationship in the emerging markets. The findings in this article
are not supportive of the theory’s basic hypothesis that higher risk is associated with a higher
level of return, this is in line with findings of Michalidis, Tsopogluo, Papanastasiou, and
Mariola, (2006) who examined the validity of the CAPM for the Greek stock market. Since
higher risk is not always associated with higher returns, international investors should be
cautious when investing in emerging markets. Nevertheless, since correlations between the
emerging markets and the world market are low, emerging markets possess interesting
diversification benefits. However, during times of financial crisis the correlations increase.
The CAPM’s prediction for the intercept is that it should equal zero. The findings of this
study do not clearly reject the above hypothesis. The CAPM states that no other measure
besides beta can explain the cross-section of returns, however in light of previous research
multifactor models were expected to add information. The findings in this study are mixed
and do not clearly support multifactor models, so in this light the CAPM cannot be
indisputably rejected.
42
Capital Asset Pricing in Emerging Market: an Empirical Investigation
The results of the analysis conducted on the emerging market stock exchanges do not
appear to clearly reject the CAPM. However, this does not mean that the data support the
CAPM. The emerging equity markets returns used in this study are not normally distributed in
most time periods. Bekaert et al. (1998) state that when faced with non-normal returns, the
mean-variance framework brakes down.
No straightforward answer can be given on the question which model performs bets.
The model that seems to perform best, depends on which country and which time period is
considered. The CAPM models do not perform well in explaining emerging stock markets
returns. Interestingly the CAPM performs better during times of financial crisis, in particular
the Local CAPM. However, it must be noted that the data period during the crisis is short. In
line with Erb, Harvey and Viskanta (1996) the evidence suggests that the CAPM shows some
merit in emerging markets, but caution is warranted. The main difficulty with the CAPM is
finding the right proxies for the risk-free rate and the market, this is even more difficult when
investigating emerging markets. The availability of data makes investigation of these markets
a difficult endeavor. Evidence showed that Size and Book to Market data help explain the
cross-section of returns, however for emerging markets the data is often not available or
incomplete. As a result the findings of Fama and French (1973) could not be replicated in this
study. Moreover, the value of beta depends on the length of the time series taken. In addition
beta changes over time. The results confirm that beta changes over time and depends on the
length of the time series. Depending on which time period is considered beta is better able to
explain returns. Furthermore, the ability to explain stock returns also depends on which model
is chosen. It is difficult to determine which time period should be considered when estimating
beta, the differences of which model to choose only add to the complexity.
This study aimed to shed some light on the applicability of the CAPM in emerging
markets, nevertheless future research is needed. Since data on emerging markets is becoming
more readily available and reliable, future research should be better able to address the
questions posed in this study on the applicability of the CAPM in emerging markets.
.
43
Hafez Barahmeh 2010
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Capital Asset Pricing in Emerging Market: an Empirical Investigation
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47
Hafez Barahmeh 2010
OVERVIEW OF THE APPENDICES
Appendix A
Companies used in analysis of Egypt
Appendix B
Companies used in analysis of Israel
Appendix C
Companies used in analysis of Morocco
Appendix D
Companies used in analysis of Turkey
Appendix E
Average excess returns
Appendix F
Betas Egypt
Appendix G
Betas Israel
Appendix H
Betas Morocco
Appendix I
Betas Turkey
48
Capital Asset Pricing in Emerging Market: an Empirical Investigation
APPENDIX A
Egyptian companies used in analysis.
ABOU KIR FERTILIZERS
ACROW MISR
ALEXANDRIA CEMENT
ALEXANDRIA FLOUR MILLS
ALEXANDRIA FOR PHARMACY
ALEXANDRIA SPNG.& WVG.
AMERIYAH CEMENT
ARAB CERAMIC
ARAB COTTON GINNING
ARAB POLIVARA SPNG.&WVG.
BISCO MISR
CAIRO PHARMACEUTICALS
CAIRO POULTRY
CANAL SHIPPING AGENCIES
COML.INTL.BANK (EGYPT)
DELTA INDUSTRIES (IDEAL)
DEVELOPMENT & ENGR.
EAST DELTA FLOUR MILLS
EASTERN TOBACCO
EFG HERMES HDG.
EGYP.CO.FOR MOBL.SVS. (MOBINIL)
EGYPT ALUMINIUM
EGYPT IRON & STEEL
EGYPTIAN CHEMICAL IND
EGYPTIAN ELECTRIC CABLE
EGYPTIAN FINL.& INDL.
EGYPTIAN GULF BANK
EGYPTIAN INTL.PHARMS. (EPICO)
EGYPTIAN KUWAITI HOLDING
EGYPTIAN MEDIA PRDN.CITY
EGYPTIAN STRCH.& GLUCOSE
EL EZZ PORCELAIN (GEMMA)
EL EZZ STEEL REBARS
EL KAHERA HOUSING & DEV.
EL NASR CLOTHES & TEXT. (KABO)
EL WATANY BANK OF EGYPT
EXPORT DEV.BK.OF EGYPT
EXTRACTED OILS DERIVATRE
GENERAL SILOS & STORAGE
GIZA GENERAL CONTRACTING
HELIOPOLIS HOUSING
HOUSING & DEV.BANK
KAFR EL-ZAIT PESTICIDES
MEDINET NASR HOUSING
MEMPHIS PHARMACEUTICALS
MENA TOURISM REAL ESTATE VESTMENT
MID.& WS.DELT.FLR.MLS.
EG1
EG2
EG3
EG4
EG5
EG6
EG7
EG8
EG9
EG10
EG11
EG12
EG13
EG14
EG15
EG16
EG17
EG18
EG19
EG20
EG21
EG22
EG23
EG24
EG25
EG26
EG27
EG28
EG29
EG30
EG31
EG32
EG33
EG34
EG35
EG36
EG37
EG38
EG39
EG40
EG41
EG42
EG43
EG44
EG45
EG46
EG47
49
Hafez Barahmeh 2010
MIDDLE EGYPT FLOUR MILLS
MISR BENI SUEF CEMENT
MISR CEMENT (QENA)
MISR CHEMICAL INDUSTRIES
MISR DUTY FREE SHOPS
MISR FOR HOTELS (HILTON)
MOHANDES INSURANCE
NATIONAL CEMENT
NATIONAL DEV.BANK
NATIONAL SCGN.BK.(NSGB)
NILE MATCH
NILE PHARMACEUTICALS
NORTH CAIRO MILLS
NTRL.GAS & MNG.PROJECT (EGYPT GAS)
OLYMPIC GP.FINL.INVS.
ORASCOM CONSTRUCTION IND
ORASCOM HOTEL HOLDINGS (OHH)
ORASCOM HOTELS AND DEV.
ORASCOM TELECOM HOLDING (OT)
ORIENTAL WEAVERS
PAINT & CHEMS.INDUSTRIES (PACHIN)
SINAI CEMENT
SIX OF OCT.DEV.& INV.
SOUTH CAIRO & GIZA MLS.& BKRS.
SOUTH VALLEY CEMENT
SUEZ CANAL BANK
SUEZ CEMENT
TORAH CEMENT
UNITED ARAB SHIPPING
UNITED HOUSING & DEV.
UPPER EGYPT CONTRACTING
UPPER EGYPT FLOUR MILLS
EG48
EG49
EG50
EG51
EG52
EG53
EG54
EG55
EG56
EG57
EG58
EG59
EG60
EG61
EG62
EG63
EG64
EG65
EG66
EG67
EG68
EG69
EG70
EG71
EG72
EG73
EG74
EG75
EG76
EG77
EG78
EG79
50
Capital Asset Pricing in Emerging Market: an Empirical Investigation
APPENDIX B
Israeli companies used in analysis.
AFRICA
AFRICA ISRAEL INDS.
ALBAAD
ALONY HETZ
ALVARION
ARAD INVESTMENT
ARAZIM
ASHTROM PROPERTIES
AUDIOCODES
AVNER L
AZORIM-INVDV.& CON.
BANK HAPOALIM B M
BANK OF JERUSALEM
BARAN
BEZEQ
BLUE SQUARE ISR
BLUE SQUARE PROPERTIES
CASTRO
CLAL INDUSTRIES
CLAL INSURANCE
COMPUGEN
COMSEC
DAN HOTELS
DAN VEHICLE
DANYA CEBUS
DELEK AUTOMOTIVE
DELEK DRILLIN L
DELEK ENERGI SYSTEMS
DELEK GROUP
DELTA
DEXIA ISRAEL BANK
DIRECT INSURANCE
DISCOUNT
DISCOUNT INVESTMENT
DOR CHEMICALS
E&M
ELBIT IMAGING
ELBIT SYSTEMS
ELCO HOLDINGS
ELECTRA
ELRON
EXPORT INVESTMENTS
FIBI
FIBRATEC MILITARY SYSTEM
FORMULA VISION
FRUTAROM
GAON REALESTATE
 DEAD - 28/10/08
IS1
IS2
IS3
IS4
IS5
IS6
IS7
IS8
IS9
IS10
IS11
IS12
IS13
IS14
IS15
IS16
IS17
IS18
IS19
IS20
IS21
IS22
IS23
IS24
IS25
IS26
IS27
IS28
IS29
IS30
IS31
IS32
IS33
IS34
IS35
IS36
IS37
IS38
IS39
IS40
IS41
IS42
IS43
IS44
IS45
IS46
IS47
51
Hafez Barahmeh 2010
GAZIT
GAZIT GLOBE
GEFEN INVESTMENTS
GMUL
GMUL VENTURES
GRANITE
HADERA PAPER
HAMLET
HAREL IN.INVS.& FNSR.
HILAN TECH
HOT TELECM.SYSTEMS
I B I INVESTMENT HOUSE
ICL
IDB DEVELOPMENT
IDB HOLDINGS
INDUSTRIAL BLDG.
INSPIRE
INTERNATIONAL 5
INVENTECH
ISAL
ISRAEL CORPORATION
ISRAEL LAND DEVELOPMENT
ISRAS
ITURAN
JERUSALEM ECONOMY
KERUR
KING
KLIL
KNAFAIM
KOOR
LEUMI
MTI
MA INDUSTRIES
MAGAL SYSTEMS
MALAM TEAM
MAMAN
MATRIX
MAYANOT EDEN
MEHADRIN
MELISRON
MENORA MIV HOLDING
MER
METALINK
MIGDAL INSURANCE
MINRAV
MIVTACH SHAMIR
MIZRAHI TEFAHOT
NICE
OCIF
OHH
OPHIR
ORBIT
 DEAD - 12/12/08
 DEAD - 05/03/09
IS48
IS49
IS50
IS51
IS52
IS53
IS54
IS55
IS56
IS57
IS58
IS59
IS60
IS61
IS62
IS63
IS64
IS65
IS66
IS67
IS68
IS69
IS70
IS71
IS72
IS73
IS74
IS75
IS76
IS77
IS78
IS79
IS80
IS81
IS82
IS83
IS84
IS85
IS86
IS87
IS88
IS89
IS90
IS91
IS92
IS93
IS94
IS95
IS96
IS97
IS98
IS99
52
Capital Asset Pricing in Emerging Market: an Empirical Investigation
ORCKIT
ORMAT
OSEM
PALRAM
PARTNER COMMUNICATIONS
PETROCHEMICAL
PLASSON INDUSTRIES
POLAR COMMUNICATION
POLAR INVESTMENTS
PRIORTECH
PROPERTY & BUILDING
QUALITAU
RADVISION
RAPAC COMM.& INFR.
RETALIX
ROBOGROUP
SALT INDUSTRIES
SANO BRUNO ENTERPRISE 1
SCAILEX CORPORATION
SCOPE METAL TRADING
SHIKUN & BINUI
SHREM FUDIM
STRAUSS GROUP
SUNY ELT.INCORP.
SUPERSOL
TEDEA TECHL.DEV.& AUMON.
TEFEN INDUSTRIAL
TELSYS
TEUZA
TEVA PHARMACEUTICAL
TOWER
TUBE
VILLA INTERNATIONAL
WALLA
YTONG IND.
ZUR
 DEAD - 02/09/08
 DEAD - 19/09/08
IS100
IS101
IS102
IS103
IS104
IS105
IS106
IS107
IS108
IS109
IS110
IS111
IS112
IS113
IS114
IS115
IS116
IS117
IS118
IS119
IS120
IS121
IS122
IS123
IS124
IS125
IS126
IS127
IS128
IS129
IS130
IS131
IS132
IS133
IS134
IS135
53
Hafez Barahmeh 2010
APPENDIX C
Moroccan companies used in analysis.
AFRIQUIA GAZ
AGMA-LAHLOU TAZI
ALUMINIUM DU MAROC
ATTIJARIWAFA BANK
AUTO NEJMA
AUTOHALL
BERLIET
BMCE BANK
BQ.MAROC.DU COM.ETDL.
BRASSERIES DU MAROC
CDM CREDIT DU MAROC
CENTRALE LAITIERE
CIMENT DU MAROC
COSUMAR
CR.IMMOBIL.ET HOTELIER
CTM
DIAC SALAF
EQDOM
FERTIMA
HOLCIM MAROC
IB MAROC.COM
LA MAROCAINE VIE
LAFARGE CIMENTS
LESIEUR CRISTAL
MAGHREB OXYGENE
MAGHREBAIL
MANAGEM
MAROC LEASING
NEXANS MAROC
ONA
SAMIR
SC.METG.D'IMITER
SC.NALE.D'INVESTISSEMENT
SCE
SONASID
TASLIF
UNIMER
WAFA ASSURANCE
 DEAD - 26/03/09
MOR1
MOR2
MOR3
MOR4
MOR5
MOR6
MOR7
MOR8
MOR9
MOR10
MOR11
MOR12
MOR13
MOR14
MOR15
MOR16
MOR17
MOR18
MOR19
MOR20
MOR21
MOR22
MOR23
MOR24
MOR25
MOR26
MOR27
MOR28
MOR29
MOR30
MOR31
MOR32
MOR33
MOR34
MOR35
MOR36
MOR37
MOR38
54
Capital Asset Pricing in Emerging Market: an Empirical Investigation
APPENDIX D
Turkish companies used in analysis.
ABANA ELEKTROMEKANIK
ACIBADEM SAGLIK HZM.VTC.
ADANA CIMENTO SANAYI 'A'
ADEL KALEMCILIK TIVSNY. 33
ADVANSA SASA PLYSR.SYI. SANAYI
AFYON CIMENTO
AK ENJE.URIM.OPKT.GRUBU
AKAL TEKSTIL SANAYI
AKBANK
AKCANSA CIMENTO SANVETC.
AKIN TEKSTIL
AKSA AKRILIK KIMYA SYI.
AKSIGORTA
AKSU ENERJI VE TICARET
AKSU IPLIK DOKUMA VE
ALARKO CARRIER SANVETC.
ALARKO GAYMEN.YATOTA.
ALARKO HOLDING
ALCATEL LUCENT TLT.TKS.
ALKIM ALKALI KIMYA
ALKIM KAGIT
ALTERNATIF YATIRIM
ALTERNATIFBANK
ALTIN YAG
ALTINYILDIZ
ANADOLU CAM SANAYII
ANADOLU EFES
ANADOLU HAYAT EMEKLILIK
ANADOLU ISUZU OMV.
ANADOLU SIGORTA
ARCELIK
ARENA BILGISAYAR
ARSAN TEKSTIL
ASELSAN
ASLAN CIMENTO
ATA YATARIM ORT
ATAKULE GAYMEN.YATOTA.
ATLAS YATIRIM ORT
AVIVA SIGORTA
AVRASYA MENKUL
AYEN ENERJI
AYGAZ
BAGFAS BANDIRMA GUBRE
BAK AMBALAJ SANVETC.
BANVIT
BATI SOKE CIMENTO SYI.
BATICIM BATI ADCT.SYI.
 DEAD - 01/05/08
TR1
TR2
TR3
TR4
TR5
TR6
TR7
TR8
TR9
TR10
TR11
TR12
TR13
TR14
TR15
TR16
TR17
TR18
TR19
TR20
TR21
TR22
TR23
TR24
TR25
TR26
TR27
TR28
TR29
TR30
TR31
TR32
TR33
TR34
TR35
TR36
TR37
TR38
TR39
TR40
TR41
TR42
TR43
TR44
TR45
TR46
TR47
55
Hafez Barahmeh 2010
BERDAN TEKSTIL SANVETC.
BESIKTAS FUTBOL
BIRLIK MENSUCAT
BISAS TEKSTIL SANVETC.
BOLU CIMENTO SANAYI
BORAVA YAPI ENDUSTRI
BORUSAN MANNESMAN BORU
BORUSAN YATIRIM VE PAZ.
BOSCH FREN SIST.SANVETC.
BOSSA TIVSNY.ISTML.
BOYASAN TEKSTIL
BOYNER BUYUK MAGAZACILIK
BRISA BDGSN.SLK.SANVETC.
BSH EV ALETLERI SANVETC.
BURCELIK BURSA CK.DOKUM
BURSA CIMENTO FABK.
CAMIS LOJISTIK DEAD - 03/09/07
CBS BOYA KIMYA SYI.VE
CBS PIT.OTO BOYA VE GRL. SANAYII
CELEBI HAVA SERVISI
CELIK HALAT VE TEL SYI.
CEMTAS CK.MAKINA SANVETC
CESME ALTINYUNOS
CEYLAN GIYAM SANVETC.
CEYTAS MADENCILIK
CIMBETON
CIMENTAS IZMIR CET.FABK.
CIMSA CIMENTO SANVETC.
CMPDKLK.TIVSNY.
CREDITWEST FACG.HZM.
DARDANEL
DEMISAS
DENIZLI CAM SANVETC.
DENTAS
DERIMOD
DEVA HOLDING
DITAS DOGAN
DOGAN BURDA DERGI YAYPZ.
DOGAN GAZETECILIK AS
DOGAN SRKGRBU.HLDG.
DOGAN YAYINCILIK HOLDING
DOGUS GE GAYMEN.YATOTA.
DOGUSAN
DURAN DOGAN BAVEABSAN.
DYO BOYA FKI.SANVETC.
ECZACIBASI YAPI GRL.
ECZACIBASI YATIRIM
ECZACIBASI YO
EDIP IPLIKN SANVETC.
EGE ENDI.VE TICARET
EGE GUBRE SANAYI
EGE PROFIL TIVSNY.
 DEAD - 20/02/09
 DEAD - 22/05/09
TR48
TR49
TR50
TR51
TR52
TR53
TR54
TR55
TR56
TR57
TR58
TR59
TR60
TR61
TR62
TR63
TR64
TR65
TR66
TR67
TR68
TR69
TR70
TR71
TR72
TR73
TR74
TR75
TR76
TR77
TR78
TR79
TR80
TR81
TR82
TR83
TR84
TR85
TR86
TR87
TR88
TR89
TR90
TR91
TR92
TR93
TR94
TR95
TR96
TR97
TR98
TR99
56
Capital Asset Pricing in Emerging Market: an Empirical Investigation
EGE SERAMIK SANVETC.
EGEPLAST
EGS GAYRIMENKUL
EIS ECZACIBASI LLAC SINAI VE FIAL.YATIRIMLAR
EMEK ELEKTRIK
EMINIS AMBALAJ
ENKA INSAAT VE SANAYI
ERBOSAN ERCIYAS BORU
EREGLI DEMIR CELIK
ERSU GIDA
ESCORT
ESEM SPOR GIYIM SANVETC.
FAVORI DINLENME YER
FED.MOGUL IZMIT PISTON
FENIS ALUMINYUM SANVETC.
FINANS FIAL.KIRALAMA
FINANS YATIRIM
FINANSBANK
FORD OTOMOTIV SANAYI
FORTIS BANK
FRIGO PAK
GALATASARAY SPORTIF
GARANTI FAKTORING HZM.
GARANTI YATIRIM
GEDIK YO
GENTAS GENEL METAL SANVETC.
GIMSAN GEDIZ IPLIK
GLOBAL YATIRIM HLDG.
GOLDAS KUYUMCULUK
GOLTAS
GOODYEAR LASTIKLERI
GRUNDIG ELEKTRONIK
 DEAD - 10/07/09
GSD HOLDING
GUBRE FABRIKALARI
GUNES SIGORTA
HACI OMER SABANCI HLDG.
HAZNEDER ATES TUGLA
HEKTAS TICARET
HURRIYET GAZETECILIK
IDAS ISA.DOSEME SANAYI
IHLAS EV
IHLAS HOLDING
INTEMA
IPEK MATBAACILIK SANVETC.
IS FINANSAL KIRALAMA
IS GAYMEN.YATOTA.
ISIKLAR AMBALAJ SANVETC.
IZMIR DEMIR CELIK SANAYI
IZOCAM TICARET VE SANAYI
KAPLAMIN AMBAL SANVETC.
KARDEMIR 'D'
KARSAN OMV.SANVETC.
TR100
TR101
TR102
TR103
TR104
TR105
TR106
TR107
TR108
TR109
TR110
TR111
TR112
TR113
TR114
TR115
TR116
TR117
TR118
TR119
TR120
TR121
TR122
TR123
TR124
TR125
TR126
TR127
TR128
TR129
TR130
TR131
TR132
TR133
TR134
TR135
TR136
TR137
TR138
TR139
TR140
TR141
TR142
TR143
TR144
TR145
TR146
TR147
TR148
TR149
TR150
TR151
57
Hafez Barahmeh 2010
KARSU TEKSTIL
KARTONSAN KARTON SANVETC.
KAV DNLK.PAZ.TIC.
KELEBEK MOBILYA
KENT GIDA MADDELERI SANVETC.
KEREVITAS GIDA SANVETC.
KLIMASAN KLIMA SANVETC.
KOC HOLDING
KONFRUT GIDA SANVETC.
KONITEKS
KONYA CIMENTO SANAYI
KORDSA SANAYIVE
KOZA ANADOLU MTL.MIE.
KRISTAL KOLA
KUTAHYA PORSELEN SYI.
LINK BILGISAYAR
LOGO YAZLIM
LUKS KADIFE
MAKINA TAKIM ENDUSTRISI
MARDIN CET.SANVETC.
MARMARIS ALTINYUNUS
MARSHALL BOYA
MARTI OTEL ISLETMELERI
MAZHAR ZORLU HOLDING
MEGES BOYA SANVETC.
MENDERES
MENSA MENSUCAT SANVETC.
MEO.TICARI VE MALI YATIRIMLAR ANONIM
MERKO GIDA SANVETC.
METEMTEKS TEKSTIL
METEMTUR OTELCILIK
MIGROS TICARET
MILPA TICARI
MUTLU AKU
MY YATIRIM ORTAKLIGI
NET HLDG.ANONIM SIRKETI
NET TURIZM TIVSNY.
NORTEL NETWKS.NETAS TKS.
NUH CIMENTO SANAYI
NUROL GAYMEN.YATOTA.
OKAN TEKSTIL SANVETC.
OLMUKSA MUKAVVA
OTOKAR OTOBUS KAROSERI
OYSANIGDE CIMENTO
OZDRI.GAYMEN.YATOTA.
PARK ELEK MADENCILIK
PARSAN
PENGUEN GIDA SANAYI
PERA GAYRIMENKUL YATOTA
PETKIM PETROKIMYA HLDG.
PETROKENT TURIZM
PETROL OFISI
 DEAD - 28/07/08
 DEAD - 08/02/07
 DEAD - 23/12/08
 DEAD - 13/11/07
TR152
TR153
TR154
TR155
TR156
TR157
TR158
TR159
TR160
TR161
TR162
TR163
TR164
TR165
TR166
TR167
TR168
TR169
TR170
TR171
TR172
TR173
TR174
TR175
TR176
TR177
TR178
TR179
TR180
TR181
TR182
TR183
TR184
TR185
TR186
TR187
TR188
TR189
TR190
TR191
TR192
TR193
TR194
TR195
TR196
TR197
TR198
TR199
TR200
TR201
TR202
TR203
58
Capital Asset Pricing in Emerging Market: an Empirical Investigation
PETUN ET VE UN
PIMAS PLASTIK
PINAR SU
PINAR SUT MAMULLERI SYI.
RAY SIGORTA
SANKO PAZARLAMA
SARKUYSAN ELKTK.BAKIR
SEKER PILIC
SEKERBANK
SELCUK GIDA
SERVE KIRTASIYE
SODA SANAYI
SOKTAS TEKSTIL SANVETC.
SONMEZ FILAMENT
SONMEZ PAMUKLU SANAYI
T DEMIR DOKUM FKI.
T TBG.BIRA VE MALT SNA.
TAT KONSERVE SANAYI
TEK-ART TURYZM ZYG.
TEKSTIL BANKASI
TEKSTIL FIAL.KIRALAMA
TESCO KIPA KITLE
TIRE KUTSAN
TKI.GARANTI BKSI.
TKI.KALKINMA BANKASI
TKI.SINAI KALK.BKSI.
TKI.SISE VE CAM FKI.
TOFAS TURK OTOM.FABK.
TRAKYA CAM SANAYI
TRANSTURK HOLDING
TUKAS GIDA SANVETC.
TUMTEKS
TUPRAS TKI.PEL.RFNE.
TURCAS PETROL
TURK EKONOMI BANKASI
TURK HAVA YOLLARI
TURK PRYS.KABLO VE SIST.
TURKCELL ILETISIM HZM.
TURKIYE IS BANKASI 'C'
UNYE CIMENTO SANVETC.
USAK SERAMIK SANAYI
USAS UCAK SERVISI
UZEL MAKINA SAN
VAKIF FINANSAL KIRALAMA
VAKIF GAYMEN.YATOTA.
VAKIF RISK
VAKIF YATRIM ORT
VAKKO TEKSTIL VE HZGY.
VESTEL ELNK.SANVETC.
VIK.KAGIT VE SELULOZ
Y VE Y GAYMEN.YATOTA.
YAPI KREDI SIGORTA
 DEAD - 07/08/08
TR204
TR205
TR206
TR207
TR208
TR209
TR210
TR211
TR212
TR213
TR214
TR215
TR216
TR217
TR218
TR219
TR220
TR221
TR222
TR223
TR224
TR225
TR226
TR227
TR228
TR229
TR230
TR231
TR232
TR233
TR234
TR235
TR236
TR237
TR238
TR239
TR240
TR241
TR242
TR243
TR244
TR245
TR246
TR247
TR248
TR249
TR250
TR251
TR252
TR253
TR254
TR255
59
Hafez Barahmeh 2010
YAPI KREDI YATIRIM
YAPI VE KREDI BANKASI
YATAS YAVYGN.SATT.
YATIRIM FINANSMAN YO
YAZICILAR HOLDING
YPK.FIAL.KIRALAMA
YPK.KORAY GAYMEN.YATOTA.
YUNSA YUNLU SANVETC.
ZORLU ENERJI
 DEAD - 10/07/09
TR256
TR257
TR258
TR259
TR260
TR261
TR262
TR263
TR264
60
Capital Asset Pricing in Emerging Market: an Empirical Investigation
APPENDIX E
Average Excess Returns
61
Hafez Barahmeh 2010
62
Capital Asset Pricing in Emerging Market: an Empirical Investigation
APPENDIX F
Global
Local
M
 Global
M
 Local
Betas Egypt
Three Moment CAPM
Four Moment CAPM
 1M
 1M
 2M
 2M
 3M
EG1
0.400073
0.443819
0.291465
-0.0277
0.23529
-0.01064
0.001151
EG2
0.246515
0.460947
0.190775
-0.01421
0.104176
0.012084
0.001774
EG3
0.194028
0.231359
0.081213
-0.02877
-0.01078
-0.00084
0.001884
EG4
0.559938
0.565431
0.519761
-0.01025
0.453217
0.009962
0.001363
EG5
0.017059
0.200396
-0.04695
-0.01632
0.033138
-0.04064
-0.00164
EG6
0.509975
0.678309
0.380397
-0.03304
0.295145
-0.00716
0.001746
EG7
0.113838
0.257929
0.11948
0.001439
0.146949
-0.0069
-0.00056
EG8
0.292532
0.29976
0.289852
-0.00068
0.28688
0.000219
6.09E-05
EG9
0.5803
0.781124
0.410943
-0.04318
0.355747
-0.02644
0.001129
EG10
0.744266
1.12462
0.641129
-0.02383
0.557125
-0.0004
0.001539
EG11
0.267605
0.299076
0.292106
0.006248
0.287892
0.007527
8.63E-05
EG12
0.345469
0.372973
0.194921
-0.03839
0.160028
-0.0278
0.000715
EG13
0.394292
0.432637
0.304927
-0.02279
0.240962
-0.00336
0.00131
EG14
0.766418
0.982104
0.811975
0.010527
0.921877
-0.02013
-0.00201
EG15
0.532739
0.630241
0.464688
-0.01735
0.521624
-0.03464
-0.00117
EG16
0.635854
0.630895
0.507635
-0.0327
0.477303
-0.0235
0.000621
EG17
0.493345
0.540769
0.394058
-0.02532
0.377917
-0.02042
0.000331
EG18
0.316652
0.506252
0.24792
-0.01753
0.240957
-0.01541
0.000143
EG19
0.10492
0.430977
0.003534
-0.02585
0.001097
-0.02511
4.99E-05
EG20
1.246439
1.148562
1.243896
-0.00062
1.210417
0.009135
0.000638
EG21
0.309831
0.742115
0.317922
0.002063
0.348477
-0.00722
-0.00063
EG22
0.348395
0.495076
0.245038
-0.02636
0.154129
0.00125
0.001862
EG23
0.744054
0.874954
0.599653
-0.03682
0.559585
-0.02466
0.000821
EG24
0.661463
0.531537
0.595884
-0.01672
0.322718
0.066229
0.005596
EG25
0.587117
0.849624
0.519933
-0.01712
0.45059
0.003941
0.001421
EG26
0.8107
0.752528
0.699956
-0.02824
0.691729
-0.02574
0.000169
EG27
0.410142
0.501179
0.17202
-0.06071
0.023229
-0.01551
0.003049
EG28
0.312367
0.417876
0.300686
-0.00298
0.319311
-0.00863
-0.00038
EG29
0.674802
0.792778
0.533016
-0.03275
0.552425
-0.03816
-0.00036
EG30
0.43374
0.998189
0.363054
-0.01803
0.287022
0.005063
0.001558
EG31
0.789357
0.471231
0.676394
-0.02877
0.593774
-0.00369
0.001692
EG32
0.865482
1.029099
0.847381
-0.00442
0.79734
0.010165
0.000954
EG33
0.902864
1.095743
0.811072
-0.02341
0.73893
-0.0015
0.001478
EG34
0.492881
0.950337
0.38923
-0.02638
0.322352
-0.00613
0.001366
EG35
0.559571
0.767408
0.400315
-0.04061
0.269488
-0.00091
0.002678
EG36
0.118912
0.435009
0.1332
0.003643
0.150514
-0.00161
-0.00036
EG37
0.402432
0.5487
0.254566
-0.03771
0.268879
-0.04205
-0.00029
EG38
0.664996
0.755583
0.539882
-0.03191
0.432467
0.000714
0.0022
EG39
0.528952
0.639551
0.369736
-0.0406
0.325752
-0.02724
0.000901
63
Hafez Barahmeh 2010
EG40
0.805519
0.891321
0.704715
-0.02571
0.652961
-0.00999
0.00106
EG41
0.946394
0.773433
EG42
0.232485
0.4889
0.788364
-0.0403
0.613657
0.012754
0.003579
0.200305
-0.00821
0.217263
-0.01336
-0.00035
EG43
0.461119
0.290685
0.419969
-0.01049
0.330404
0.016705
0.001835
EG44
0.850612
0.90076
0.754656
-0.02447
0.634092
0.012142
0.00247
EG45
0.271124
0.241408
0.330208
0.015067
0.231061
0.045174
0.002031
EG46
0.6583
0.710794
0.584977
-0.0187
0.533956
-0.0032
0.001045
EG47
0.419161
0.514287
0.361246
-0.01477
0.275348
0.011316
0.00176
EG48
0.800887
0.582156
0.691458
-0.02791
0.600044
-0.00015
0.001873
EG49
0.254884
0.48344
0.192344
-0.01606
0.167767
-0.00887
0.000484
EG50
0.112939
0.216734
0.062174
-0.01173
0.099888
-0.02225
-0.00069
EG51
0.721672
0.935408
0.562886
-0.04049
0.453662
-0.00732
0.002237
EG52
0.295455
0.351861
0.269629
-0.00659
0.203829
0.013381
0.001347
EG53
0.278129
0.201289
0.288763
0.002712
0.168674
0.039179
0.00246
EG54
0.283501
0.382934
0.237698
-0.01168
0.215754
-0.00502
0.00045
EG55
0.336255
0.620825
0.218193
-0.03011
0.21537
-0.02925
5.78E-05
EG56
0.355551
0.405841
0.320051
-0.00905
0.326985
-0.01116
-0.00014
EG57
0.504093
0.559496
0.420742
-0.02126
0.451105
-0.03048
-0.00062
EG58
0.797747
0.479705
0.718801
-0.02013
0.582429
0.02128
0.002794
EG59
0.080991
0.18201
-0.02714
-0.02757
-0.10307
-0.00452
0.001555
EG60
0.643116
0.799506
0.476306
-0.04254
0.379812
-0.01324
0.001977
EG61
0.349537
0.413117
0.274639
-0.0191
0.15727
0.016542
0.002404
EG62
0.661574
0.725736
0.570608
-0.0232
0.579807
-0.02599
-0.00019
EG63
0.896467
0.934404
0.744755
-0.03869
0.717497
-0.03041
0.000558
EG64
0.537156
0.584915
0.432573
-0.02667
0.475314
-0.03965
-0.00088
EG65
0.659618
0.911907
0.684045
0.006229
0.755544
-0.01548
-0.00147
EG66
1.063803
1.093086
1.065576
0.000452
1.067417
-0.00011
-3.77E-05
EG67
0.463238
0.538517
0.377689
-0.02182
0.286901
0.005754
0.00186
EG68
0.360747
0.547757
0.272007
-0.02263
0.312774
-0.03501
-0.00084
EG69
0.519796
0.491887
0.449611
-0.01712
0.376779
0.004099
0.001389
EG70
1.00139
1.003412
0.821837
-0.04579
0.780355
-0.03319
0.00085
EG71
0.648254
0.756239
0.518629
-0.03306
0.459001
-0.01495
0.001221
EG72
0.663709
0.785741
0.528184
-0.03414
0.435611
-0.00642
0.001871
EG73
0.552598
0.483183
0.503221
-0.01259
0.497062
-0.01072
0.000126
EG74
0.296674
0.541121
0.252196
-0.01134
0.244315
-0.00895
0.000161
EG75
0.371708
0.388685
0.303604
-0.01737
0.206868
0.012009
0.001982
EG76
0.784276
0.947549
0.658123
-0.03217
0.530644
0.006507
0.002609
EG77
0.704006
0.880763
0.600185
-0.02648
0.58479
-0.0218
0.000315
EG78
0.734741
0.807034
0.630121
-0.02668
0.551655
-0.00285
0.001607
EG79
0.514953
0.531282
0.40049
-0.02919
0.353851
-0.01503
0.000955
Max
1.246439
1.148562
1.243896
0.015067
1.210417
0.066229
0.005596
Min
0.017059
0.18201
-0.04695
-0.06071
-0.10307
-0.04205
-0.00201
64
Capital Asset Pricing in Emerging Market: an Empirical Investigation
APPENDIX G
Global
M
 Global
Local
M
 Local
Betas Israel
Three Moment CAPM
 1M
 2M
Four Moment CAPM
 1M
 2M
 3M
IS1
0.888461
0.877784
1.113565
0.057403
1.353716
-0.01552
-0.00492
IS2
1.047913
0.868343
1.092389
0.011342
1.106639
0.007015
-0.00029
IS3
0.692459
0.737258
0.785812
0.023805
0.828759
0.010764
-0.00088
IS4
1.006188
0.601904
1.153705
0.037618
1.347781
-0.02132
-0.00398
IS5
1.421669
0.985219
1.346174
-0.01896
1.476379
-0.05637
-0.00252
IS6
0.4805
0.479533
0.505484
0.006371
0.591801
-0.01984
-0.00177
IS7
1.245116
0.784305
1.278086
0.008408
1.374755
-0.02095
-0.00198
IS8
0.643133
0.538892
0.680282
0.009473
0.837559
-0.03829
-0.00322
IS9
1.085977
0.965413
1.321291
0.057292
1.600616
-0.02385
-0.00531
IS10
0.475833
0.443927
0.552809
0.019629
0.509847
0.032675
0.00088
IS11
1.435737
0.963956
1.576242
0.03583
1.711829
-0.00534
-0.00278
IS12
0.788564
0.675696
0.857945
0.017693
0.954908
-0.01175
-0.00199
IS13
0.384962
0.344084
0.360628
-0.00621
0.39972
-0.01808
-0.0008
IS14
1.361116
0.983129
1.547562
0.047545
1.564255
0.042476
-0.00034
IS15
0.456201
0.527647
0.496642
0.010313
0.506915
0.007193
-0.00021
IS16
0.67783
0.570998
0.781314
0.026389
0.853137
0.004579
-0.00147
IS17
0.446461
0.642721
0.481319
0.008889
0.539752
-0.00886
-0.0012
IS18
0.749372
0.74416
0.854731
0.026867
0.852595
0.027516
4.38E-05
IS19
1.05952
0.894192
1.16175
0.026069
1.306854
-0.01799
-0.00297
IS20
0.811821
0.800107
0.88048
0.017508
1.032913
-0.02878
-0.00312
IS21
0.523011
0.818086
0.439589
-0.02016
0.536956
-0.04802
-0.00183
IS22
0.579025
0.730605
0.609152
0.007682
0.726826
-0.02805
-0.00241
IS23
0.699642
0.785662
0.69641
-0.00082
0.831338
-0.0418
-0.00276
IS24
0.739275
0.640379
0.734081
-0.00132
0.853567
-0.03761
-0.00245
IS25
0.914837
0.970005
0.9355
0.005269
1.184062
-0.07021
-0.00509
IS26
0.841755
0.697423
0.903459
0.015735
0.95768
-0.00073
-0.00111
IS27
0.541044
0.519319
0.629366
0.022523
0.605673
0.029718
0.000485
IS28
0.607926
0.691236
0.633525
0.006528
0.689442
-0.01045
-0.00115
IS29
0.674727
0.698923
0.865185
0.048568
1.015424
0.002945
-0.00308
IS30
0.841752
0.760448
0.756656
-0.0217
0.871766
-0.05666
-0.00236
IS31
0.748999
0.398696
0.819069
0.017868
0.783142
0.028778
0.000736
IS32
1.111427
0.785378
1.21855
0.027317
1.180129
0.038984
0.000787
IS33
0.759808
0.696531
0.837799
0.019888
0.910871
-0.0023
-0.0015
IS34
0.920576
0.856046
1.051954
0.033502
1.221288
-0.01792
-0.00347
IS35
0.237042
0.324363
0.230533
-0.00166
0.248187
-0.00702
-0.00036
IS36
0.923864
0.925733
0.97885
0.014022
0.996283
0.008728
-0.00036
IS37
1.230023
0.946199
1.425349
0.049809
1.502865
0.02627
-0.00159
65
Hafez Barahmeh 2010
IS38
0.507617
0.426447
0.501815
-0.00148
0.531964
-0.01064
-0.00062
IS39
0.94906
0.715187
IS40
1.015449
0.721488
1.015138
0.01685
1.129178
0.029002
1.080127
-0.00289
-0.00133
1.16782
0.017267
-0.00079
IS41
1.290131
1.025562
1.370023
0.020373
1.41236
0.007516
-0.00087
IS42
0.530472
0.537251
0.54267
0.003111
0.56117
-0.00251
-0.00038
IS43
0.974544
IS44
0.679322
0.908656
1.034545
0.015301
1.135885
-0.01547
-0.00208
0.590009
0.706051
0.006816
0.758184
-0.00902
-0.00107
IS45
0.965311
0.796903
1.075632
0.028136
1.083449
0.025762
-0.00016
IS46
0.724501
0.659794
0.827718
0.026321
0.84953
0.019697
-0.00045
IS47
0.431669
0.409384
0.521516
0.022911
0.597529
-0.00017
-0.00156
IS48
1.097867
0.781674
1.27225
0.042217
1.313023
0.03054
-0.00077
IS49
0.866819
0.524186
0.936601
0.017795
1.026442
-0.00949
-0.00184
IS50
0.531692
0.499302
0.54725
0.003967
0.537212
0.007016
0.000206
IS51
0.435436
0.759536
0.277241
-0.03986
0.732274
-0.17122
-0.00884
IS52
0.437484
0.256058
0.463733
0.006693
0.556113
-0.02133
-0.00189
IS53
0.906066
0.601912
1.009825
0.026459
0.995126
0.030923
0.000301
IS54
0.88671
0.701226
0.991312
0.026674
1.00481
0.022575
-0.00028
IS55
0.982352
0.863106
0.99716
0.003776
1.124584
-0.03492
-0.00261
IS56
0.753178
0.7263
0.74355
-0.00246
0.804688
-0.02102
-0.00125
IS57
0.731643
0.575473
0.749379
0.004523
0.838351
-0.0225
-0.00182
IS58
0.885592
0.795717
0.967478
0.020881
1.024538
0.003554
-0.00117
IS59
0.792125
0.791625
0.698534
-0.02387
0.757874
-0.04189
-0.00122
IS60
1.117311
0.826857
1.18385
0.016968
1.210444
0.008892
-0.00055
IS61
0.738886
0.787436
0.800991
0.015837
0.988429
-0.04108
-0.00384
IS62
0.790257
0.8204
0.899087
0.027752
0.966442
0.007299
-0.00138
IS63
0.763904
0.751756
0.85502
0.023235
0.969982
-0.01168
-0.00236
IS64
0.550819
0.659186
0.555451
0.001181
0.643732
-0.02563
-0.00181
IS65
0.884429
0.8537
0.944681
0.015365
0.982801
0.003789
-0.00078
IS66
0.17679
0.345974
0.135049
-0.01064
0.221997
-0.03705
-0.00178
IS67
0.876936
0.587722
0.840146
-0.00938
0.834117
-0.00755
0.000124
IS68
1.386975
1.135248
1.491016
0.026531
1.633175
-0.01664
-0.00291
IS69
0.936136
0.836494
0.90276
-0.00851
0.920322
-0.01384
-0.00036
IS70
0.295383
0.352177
0.343704
0.012322
0.410574
-0.00798
-0.00137
IS71
0.961892
0.790338
0.975025
0.003349
0.987276
-0.00037
-0.00025
IS72
1.342981
0.86848
1.497643
0.03944
1.623354
0.001265
-0.00258
IS73
0.631785
0.491985
0.638556
0.001727
0.61323
0.009417
0.000519
IS74
0.34438
0.591863
0.348564
0.001067
0.342707
0.002845
0.00012
IS75
0.599382
0.368726
0.608995
0.002451
0.589907
0.008248
0.000391
IS76
0.463445
0.693187
0.405771
-0.01471
0.491261
-0.04067
-0.00175
IS77
1.619828
1.153864
1.673646
0.013724
1.786438
-0.02053
-0.00231
IS78
0.826897
0.693912
0.876486
0.012646
1.003462
-0.02591
-0.0026
IS79
0.85784
0.853059
0.791701
-0.01687
1.019332
-0.08599
-0.00466
IS80
0.970831
0.77922
1.006726
0.009154
1.00198
0.010595
9.72E-05
IS81
0.572193
0.249933
0.520665
-0.01295
0.523414
-0.01374
-5.32E-05
IS82
0.589114
0.704275
0.623034
0.00865
0.736398
-0.02578
-0.00232
66
Capital Asset Pricing in Emerging Market: an Empirical Investigation
IS83
0.288432
0.302118
0.267166
-0.00542
0.341338
-0.02795
-0.00152
IS84
0.821135
0.509213
0.832668
0.002941
0.839176
0.000965
-0.00013
IS85
1.178023
0.84807
1.190338
0.00314
1.156062
0.013549
0.000702
IS86
0.616972
0.542042
0.527484
-0.02282
0.596915
-0.0439
-0.00142
IS87
0.799676
0.554005
0.872334
0.018337
0.865825
0.02029
0.000132
IS88
0.796842
0.636977
0.880814
0.021413
0.946869
0.001355
-0.00135
IS89
1.030133
1.163604
1.098446
0.01742
1.218143
-0.01893
-0.00245
IS90
1.199626
1.074377
1.031926
-0.04276
1.114916
-0.06797
-0.0017
IS91
0.670092
0.680839
0.715108
0.011479
0.819054
-0.02009
-0.00213
IS92
0.726532
0.733083
0.728706
0.000554
0.735415
-0.00148
-0.00014
IS93
1.047175
0.980026
1.0924
0.011533
1.138734
-0.00254
-0.00095
IS94
0.679166
0.558418
0.787309
0.027577
0.803621
0.022623
-0.00033
IS95
1.192965
0.880695
1.235389
0.010818
1.205963
0.019754
0.000603
IS96
1.192643
0.798359
1.264546
0.018336
1.336154
-0.00341
-0.00147
IS97
0.757564
0.583508
0.861181
0.026423
0.894766
0.016224
-0.00069
IS98
0.627104
0.474993
0.628035
0.000237
0.642995
-0.00431
-0.00031
IS99
0.457114
0.465128
0.459155
0.00052
0.474432
-0.00412
-0.00031
IS100
1.266993
1.084368
1.317404
0.011713
1.443995
-0.02394
-0.00234
IS101
0.927657
0.772375
0.841552
-0.02196
0.849046
-0.02423
-0.00015
IS102
0.463346
0.567306
0.503216
0.010167
0.538019
-0.0004
-0.00071
IS103
0.754822
0.590541
0.860277
0.026891
0.939873
0.002721
-0.00163
IS104
0.557954
0.720905
0.643601
0.021526
0.695091
0.006728
-0.001
IS105
1.410118
1.046581
1.518797
0.027714
1.454657
0.047191
0.001314
IS106
0.908386
0.526956
0.99319
0.021625
0.936285
0.038906
0.001166
IS107
0.470532
0.584239
0.450625
-0.00508
0.581698
-0.04488
-0.00269
IS108
0.614816
0.697867
0.67492
0.015327
0.771827
-0.0141
-0.00199
IS109
1.090199
1.224872
1.235168
0.036968
1.43027
-0.02228
-0.004
IS110
0.966686
0.789791
1.028042
0.015646
1.066017
0.004114
-0.00078
IS111
0.596383
0.793684
0.627872
0.00803
0.714356
-0.01823
-0.00177
IS112
0.995842
0.4618
1.15239
0.034543
1.143455
0.03686
0.000152
IS113
0.833668
0.85979
0.849919
0.004144
0.97239
-0.03305
-0.00251
IS114
1.014718
0.801958
1.11079
0.024499
1.21063
-0.00582
-0.00205
IS115
0.744767
0.77781
0.775097
0.007734
0.747894
0.015995
0.000557
IS116
0.482758
0.667945
0.510771
0.007143
0.600804
-0.0202
-0.00184
IS117
0.357863
0.374352
0.38754
0.007568
0.404388
0.002451
-0.00035
IS118
0.631947
0.754091
0.678407
0.011849
0.740121
-0.00688
-0.00126
IS119
1.204565
0.680904
1.285676
0.020684
1.307131
0.014169
-0.00044
IS120
0.876024
0.659087
1.092362
0.055167
1.298327
-0.00738
-0.00422
IS121
0.628216
0.697615
0.577721
-0.01288
0.655865
-0.03661
-0.0016
IS122
0.583043
0.637891
0.713673
0.033311
0.815725
0.002322
-0.00209
IS123
0.983675
0.98663
1.049774
0.016855
1.100956
0.001313
-0.00105
IS124
0.549807
0.511759
0.59125
0.010568
0.619034
0.002131
-0.00057
IS125
0.769845
0.556798
0.764516
-0.00136
0.720018
0.012153
0.000912
IS126
0.358179
0.341158
0.353991
-0.00107
0.372269
-0.00662
-0.00037
IS127
0.751604
0.750114
0.81454
0.016049
0.944347
-0.02337
-0.00266
67
Hafez Barahmeh 2010
IS128
0.600213
0.602779
0.630942
0.007836
0.738591
-0.02485
-0.00221
IS129
0.501213
0.596829
0.507302
0.001553
0.484991
0.008328
0.000457
IS130
1.420398
1.42137
1.421824
0.000364
1.650601
-0.06908
-0.00468
IS131
0.623856
0.486653
0.590846
-0.00842
0.600996
-0.0115
-0.00021
IS132
0.550131
0.429097
0.547477
-0.00068
0.529091
0.004907
0.000377
IS133
1.17607
1.566296
1.265084
0.022699
1.414312
-0.02262
-0.00306
IS134
0.500766
0.473872
0.537758
0.009433
0.612088
-0.01314
-0.00152
IS135
0.88798
0.686129
0.944931
0.014523
1.164863
-0.05226
-0.00451
Max
1.619828
1.566296
1.673646
0.057403
1.786438
0.047191
0.001314
Min
0.17679
0.249933
0.135049
-0.04276
0.221997
-0.17122
-0.00884
68
Capital Asset Pricing in Emerging Market: an Empirical Investigation
APPENDIX H
Global
Local
M
 Global
M
 Local
MOR1
0.225333
MOR2
MOR3
Betas Morocco
Three Moment CAPM
Four Moment CAPM
 1M
 2M
 1M
 2M
 3M
0.833817
0.22425
-0.00028
0.250502
-0.00825
-0.00054
0.050987
0.452855
0.025683
-0.00645
0.036128
-0.00963
-0.00021
0.09828
0.555598
0.05506
-0.01102
0.089317
-0.02142
-0.0007
MOR4
0.167423
0.95739
0.149226
-0.00464
0.194909
-0.01851
-0.00094
MOR5
0.354198
0.43308
0.245976
-0.0276
0.313106
-0.04798
-0.00138
MOR6
0.140437
0.619894
0.032722
-0.02747
0.161931
-0.0667
-0.00265
MOR7
0.270067
0.692426
0.217126
-0.0135
0.336683
-0.04981
-0.00245
MOR8
0.24192
0.823991
0.204882
-0.00945
0.274244
-0.03051
-0.00142
MOR9
0.337339
0.67107
0.373417
0.0092
0.341623
0.018855
0.000651
MOR10
0.132111
0.627415
0.060424
-0.01828
0.0935
-0.02833
-0.00068
MOR11
0.299296
0.803116
0.299295
-2.51E-07
0.32829
-0.00881
-0.00059
MOR12
0.095411
0.560523
0.098413
0.000766
0.168376
-0.02048
-0.00143
MOR13
0.316186
0.882308
0.198177
-0.03009
0.197652
-0.02993
1.07E-05
MOR14
0.242754
0.563258
0.196923
-0.01169
0.18055
-0.00672
0.000335
MOR15
0.176735
0.878996
0.074094
-0.02617
0.14779
-0.04855
-0.00151
MOR16
0.221551
0.524615
0.149872
-0.01828
0.151274
-0.0187
-2.87E-05
MOR17
0.3536
0.753377
0.236931
-0.02975
0.284334
-0.04415
-0.00097
MOR18
0.141161
0.752832
0.125461
-0.004
0.19605
-0.02544
-0.00145
MOR19
0.180252
0.285035
0.092597
-0.02235
0.047339
-0.00861
0.000927
MOR20
0.245477
1.007424
0.1513
-0.02402
0.166839
-0.02873
-0.00032
MOR21
0.295605
0.683835
0.279667
-0.00401
0.330395
-0.01857
-0.00098
MOR22
0.19613
0.632843
0.124916
-0.01812
0.169061
-0.03142
-0.0009
MOR23
0.156852
0.93766
0.117682
-0.00999
0.195604
-0.03365
-0.0016
MOR24
0.148216
0.486799
0.107356
-0.01042
0.119454
-0.01409
-0.00025
MOR25
0.26074
0.69019
0.244727
-0.00408
0.286597
-0.0168
-0.00086
MOR26
0.129435
0.510973
0.071599
-0.01475
0.132971
-0.03339
-0.00126
MOR27
0.228513
1.156863
0.153186
-0.01921
0.235407
-0.04418
-0.00168
MOR28
0.360118
0.677771
0.286533
-0.01877
0.339494
-0.03485
-0.00109
MOR29
0.249812
0.501485
0.182579
-0.01715
0.250633
-0.03781
-0.00139
MOR30
0.280263
1.071753
0.245085
-0.00897
0.366135
-0.04573
-0.00248
MOR31
0.282872
1.007782
0.23994
-0.01095
0.281693
-0.02363
-0.00086
MOR32
0.105439
0.646025
0.001716
-0.02645
0.007098
-0.02808
-0.00011
MOR33
0.154485
0.974003
0.126903
-0.00703
0.18946
-0.02603
-0.00128
MOR34
0.140006
0.47664
0.150007
0.00255
0.219795
-0.01864
-0.00143
MOR35
0.231212
0.928695
0.146888
-0.0215
0.162276
-0.02618
-0.00032
MOR36
0.121428
0.312827
0.112246
-0.00234
0.139235
-0.01054
-0.00055
MOR37
0.180442
0.361775
0.158736
-0.00556
0.202148
-0.01825
-0.00085
MOR38
0.107384
1.070781
0.030636
-0.01957
0.18247
-0.06568
-0.00311
Max
0.360118 1.156863 0.373417
0.0092
0.366135 0.018855 0.000927
69
Hafez Barahmeh 2010
Min
0.050987 0.285035 0.001716
-0.03009
Local
M
 Global
M
 Local
Three Moment CAPM
 2M
 1M
-0.00311
Betas Turkey
APPENDIX I
Global
0.007098 -0.0667
Four Moment CAPM
 1M
 2M
 3M
TR1
0.552807
0.50845
0.559973
0.001823
0.59551
-0.00889
-0.000722821
TR2
0.867433
0.601347
0.827669
-0.01012
0.885612
-0.02758
-0.001178562
TR3
1.115658
0.765378
1.01065
-0.02672
1.009251
-0.02629
2.85E-05
TR4
0.976147
0.72435
0.892796
-0.02121
0.896394
-0.02229
-7.32E-05
TR5
1.008788
0.791744
0.929119
-0.02027
0.88807
-0.0079
0.000834919
TR6
0.94888
0.657571
0.893869
-0.014
0.920215
-0.02194
-0.000535875
TR7
1.201764
0.819487
1.223112
0.005431
1.098143
0.043098
0.002541852
TR8
1.003727
0.731602
0.95579
-0.0122
0.933094
-0.00536
0.000461618
TR9
1.352716
1.055181
1.329079
-0.00601
1.3233
-0.00427
0.000117546
TR10
1.324487
0.946842
1.32269
-0.00046
1.338397
-0.00519
-0.00031949
TR11
1.117433
0.726864
1.044832
-0.01847
0.960782
0.006863
0.001709583
TR12
1.04736
0.757413
1.048538
0.0003
1.071837
-0.00672
-0.000473914
TR13
1.54728
1.031638
1.510749
-0.00929
1.544215
-0.01938
-0.000680698
TR14
1.165545
0.82656
1.174207
0.002204
1.07495
0.03212
0.002018868
TR15
1.130564
0.741092
1.020556
-0.02799
0.97258
-0.01353
0.000975837
TR16
0.977751
0.748305
0.929481
-0.01228
0.991097
-0.03085
-0.001253264
TR17
0.905462
0.688222
0.8125
-0.02365
0.89798
-0.04942
-0.001738643
TR18
1.077106
0.836
1.086681
0.002436
1.056404
0.011562
0.000615843
TR19
1.233156
0.96525
1.184607
-0.01235
1.109814
0.010192
0.001521281
TR20
1.150483
0.786579
1.092327
-0.0148
1.108474
-0.01966
-0.000328435
TR21
1.260274
0.759399
1.135439
-0.03166
0.986413
0.013082
0.003018906
TR22
0.990049
0.880283
0.893938
-0.02445
0.874628
-0.01863
0.000392747
TR23
1.437428
1.08391
1.417059
-0.00518
1.333307
0.020062
0.001703511
TR24
0.981958
0.757409
0.947153
-0.00886
0.956943
-0.01181
-0.000199133
TR25
0.922196
0.779766
0.837503
-0.02155
0.996379
-0.06943
-0.003231512
TR26
1.107358
0.799544
1.094457
-0.00328
1.114082
-0.0092
-0.000399168
TR27
1.070353
0.75162
1.039426
-0.00787
1.045365
-0.00966
-0.000120795
TR28
1.243883
0.938824
1.225566
-0.00466
1.235642
-0.0077
-0.000204943
TR29
1.349768
0.895905
1.220311
-0.03294
1.249234
-0.04165
-0.000588285
TR30
1.304305
0.952945
1.281833
-0.00572
1.270172
-0.0022
0.000237187
TR31
1.184113
0.995196
1.150764
-0.00848
1.211295
-0.02673
-0.001231213
TR32
1.066846
0.842375
1.019365
-0.01208
1.11591
-0.04118
-0.001963712
TR33
0.682832
0.741072
0.70074
0.004556
0.790491
-0.0225
-0.001825521
TR34
1.151422
0.850113
1.058469
-0.02365
1.094648
-0.03455
-0.000735864
TR35
1.008618
0.733206
0.880339
-0.03264
0.772306
-7.28E-05
0.002197397
TR36
1.075
0.755607
1.104574
0.007524
1.137546
-0.00241
-0.000670658
TR37
1.123691
0.865563
1.106061
-0.00425
1.125495
-0.0098
-0.000363592
TR38
1.316451
0.964747
1.334739
0.004653
1.183346
0.050284
0.003079324
TR39
1.086927
0.567868
0.978026
-0.02771
0.813905
0.021762
0.003338205
TR40
1.066089
0.702585
0.981786
-0.02145
0.943119
-0.00979
0.000786486
70
Capital Asset Pricing in Emerging Market: an Empirical Investigation
TR41
1.249094
0.838979
1.186056
-0.01604
1.14788
-0.00453
0.000776498
TR42
1.118135
0.821905
1.083611
-0.00878
1.095059
-0.01223
-0.000232858
TR43
1.09192
0.834194
0.963351
-0.03271
0.902945
-0.0145
0.001228649
TR44
0.99762
0.616204
0.904151
-0.02378
0.851201
-0.00782
0.001076997
TR45
1.154946
0.828181
1.011978
-0.03637
0.900463
-0.00276
0.002268211
TR46
1.329157
0.80944
1.173654
-0.03956
1.097946
-0.01674
0.001539883
TR47
0.745458
0.660391
0.735042
-0.00265
0.77955
-0.01607
-0.000905286
TR48
1.143852
0.800812
1.027064
-0.02968
1.00337
-0.02254
0.000481954
TR49
0.736383
0.656936
0.600841
-0.03261
0.464194
0.006375
0.00255443
TR50
1.068692
0.581665
0.969831
-0.02515
0.961866
-0.02275
0.000162012
TR51
0.872741
0.730769
0.650643
-0.0565
0.625194
-0.04883
0.000517631
TR52
0.946294
0.749421
0.896632
-0.01264
0.94748
-0.02796
-0.001034243
TR53
1.211519
0.869698
1.174363
-0.00945
1.068489
0.022459
0.002153473
TR54
1.156999
0.790423
1.037759
-0.03034
0.965522
-0.00856
0.001469302
TR55
1.084774
0.842734
1.079761
-0.00128
1.100926
-0.00766
-0.000430503
TR56
1.164933
0.72145
1.012474
-0.0387
0.919933
-0.01088
0.001878117
TR57
0.689406
0.671658
0.688069
-0.00034
0.634663
0.015757
0.00108627
TR58
0.921085
0.663741
0.844797
-0.01941
0.905299
-0.03764
-0.001230599
TR59
1.351691
1.010044
1.207719
-0.03663
1.245656
-0.04806
-0.000771629
TR60
1.01101
0.780898
0.872249
-0.0353
0.840298
-0.02567
0.00064988
TR61
0.909998
0.60246
0.868241
-0.01062
0.847408
-0.00434
0.000423735
TR62
0.880188
0.605758
0.820796
-0.01511
0.709471
0.018445
0.002264341
TR63
0.7075
0.491215
0.674244
-0.00846
0.68184
-0.01075
-0.00015451
TR64
0.537702
0.484897
0.543804
0.001553
0.565373
-0.00495
-0.00043871
TR65
1.190481
0.753722
0.96412
-0.05759
0.849843
-0.02314
0.002324376
TR66
0.985712
0.776804
0.828673
-0.03995
0.781839
-0.02584
0.000952606
TR67
1.368579
0.869624
1.290579
-0.01984
1.173674
0.015392
0.002377827
TR68
0.99534
0.862686
0.870907
-0.03166
0.942175
-0.05314
-0.001449567
TR69
1.072388
0.781109
1.025175
-0.01201
1.087668
-0.03085
-0.00127111
TR70
1.13451
0.787065
0.97833
-0.03973
0.90521
-0.0177
0.001487242
TR71
1.14259
0.661339
0.935956
-0.05257
0.724882
0.01105
0.004293234
TR72
1.345997
1.008587
1.298528
-0.01208
1.230169
0.008527
0.001390402
TR73
1.170502
0.745743
1.101688
-0.01751
1.071283
-0.00834
0.000618437
TR74
0.897185
0.528772
0.915928
0.004769
0.945874
-0.00426
-0.000609091
TR75
1.153348
0.83642
1.163901
0.002685
1.261731
-0.0268
-0.00198986
TR76
1.095364
0.736802
0.956939
-0.03522
0.83365
0.001944
0.002507678
TR77
0.829454
0.702964
0.678419
-0.03843
0.537263
0.004121
0.002871098
TR78
1.144841
0.817734
1.041616
-0.02626
1.005135
-0.01527
0.000742008
TR79
0.991734
0.674402
0.890727
-0.0257
0.892095
-0.02611
-2.78E-05
TR80
1.01282
0.758708
0.966019
-0.01191
0.914029
0.003763
0.001057461
TR81
1.014539
0.639628
0.906857
-0.0274
0.923997
-0.03256
-0.00034864
TR82
1.258379
0.962953
1.096608
-0.04116
0.998711
-0.01165
0.001991212
TR83
1.32586
0.800602
1.179768
-0.03717
1.084633
-0.00849
0.001935038
TR84
1.096663
0.73738
0.98172
-0.02924
0.922845
-0.0115
0.001197518
TR85
0.923752
0.910357
0.850312
-0.01868
0.85563
-0.02029
-0.000108161
71
Hafez Barahmeh 2010
TR86
1.277642
1.063872
1.189866
-0.02233
1.156715
-0.01234
0.000674295
TR87
1.137468
1.112787
1.106135
-0.00797
1.198741
-0.03588
-0.0018836
TR88
1.315913
1.161838
1.278855
-0.00943
1.446172
-0.05986
-0.003403202
TR89
1.138354
0.848616
0.967298
-0.04352
0.911963
-0.02684
0.001125516
TR90
1.088129
0.762161
0.933564
-0.03932
0.867869
-0.01952
0.001336236
TR91
1.283009
0.71411
1.181679
-0.02578
1.031256
0.01956
0.003059603
TR92
1.211843
0.882602
1.140292
-0.0182
1.093461
-0.00409
0.000952542
TR93
1.266927
0.85663
1.222521
-0.0113
1.2828
-0.02947
-0.001226067
TR94
1.104613
0.838538
1.096993
-0.00194
1.173773
-0.02508
-0.001561708
TR95
1.221553
0.77288
1.239717
0.004621
1.225306
0.008965
0.000293125
TR96
1.026039
0.712657
0.847716
-0.04537
0.799252
-0.03076
0.000985756
TR97
0.980812
0.74083
0.947586
-0.00845
0.912626
0.002084
0.000711094
TR98
1.30173
0.77259
1.230062
-0.01823
1.008235
0.048628
0.004511945
TR99
1.087431
0.508207
0.957355
-0.03309
0.780609
0.02018
0.003594989
TR100
1.344467
0.847475
1.213466
-0.03333
1.11027
-0.00222
0.002099014
TR101
1.107885
0.805509
1.035407
-0.01844
1.060705
-0.02606
-0.000514562
TR102
1.139912
0.763681
1.007708
-0.03363
0.943474
-0.01427
0.001306502
TR103
1.035271
0.832901
1.080529
0.011514
1.243585
-0.03763
-0.003316552
TR104
1.097197
0.795249
1.000243
-0.02467
0.975388
-0.01717
0.000505543
TR105
0.76714
0.57399
0.607638
-0.04058
0.963979
-0.14798
-0.007247944
TR106
1.249225
0.820191
1.171448
-0.01979
1.170597
-0.01953
1.73E-05
TR107
0.819132
0.656001
0.698835
-0.0306
0.662663
-0.0197
0.000735738
TR108
1.446824
0.973652
1.379132
-0.01722
1.412093
-0.02716
-0.000670414
TR109
1.131872
0.67508
1.069717
-0.01581
1.004457
0.003857
0.001327375
TR110
1.396938
0.781346
1.222665
-0.04434
1.205237
-0.03908
0.000354486
TR111
1.165423
0.77164
1.043377
-0.03105
0.974564
-0.01031
0.00139965
TR112
1.384726
0.894184
1.295426
-0.02272
1.332462
-0.03388
-0.000753319
TR113
1.432947
0.695272
1.229406
-0.05178
1.175638
-0.03558
0.001093633
TR114
0.846299
0.650117
0.73905
-0.02729
0.680109
-0.00952
0.001198843
TR115
1.351064
0.74824
1.135115
-0.05494
1.09049
-0.04149
0.000907661
TR116
1.046245
0.80347
0.905558
-0.03579
0.891391
-0.03152
0.000288143
TR117
1.000942
0.82758
0.95047
-0.01284
0.936446
-0.00861
0.000285245
TR118
1.218249
0.92592
1.212966
-0.00134
1.159226
0.014854
0.001093066
TR119
1.330563
0.954805
1.259253
-0.01814
1.289139
-0.02715
-0.00060788
TR120
1.267135
0.757789
1.252903
-0.00362
1.133295
0.03243
0.002432806
TR121
0.880343
0.564159
0.636252
-0.05871
0.413018
0.004974
0.004173607
TR122
1.32123
0.963625
1.27099
-0.01278
1.160294
0.020583
0.002251534
TR123
1.028288
0.809859
1.000008
-0.0072
0.964439
0.003526
0.000723475
TR124
1.058947
0.685546
0.964593
-0.024
0.844416
0.012218
0.002444404
TR125
0.858919
0.660348
0.812175
-0.01189
0.830443
-0.0174
-0.000371573
TR126
1.318466
0.873788
1.241657
-0.01954
1.226225
-0.01489
0.000313891
TR127
1.480868
1.160636
1.172962
-0.07833
1.028042
-0.03465
0.002947663
TR128
1.327473
0.958231
1.243577
-0.02134
1.200082
-0.00823
0.000884669
TR129
1.243376
0.712499
1.13321
-0.02803
1.162445
-0.03684
-0.000594625
TR130
1.027408
0.762091
0.935426
-0.0234
0.969486
-0.03367
-0.000692782
72
Capital Asset Pricing in Emerging Market: an Empirical Investigation
TR131
1.134351
0.895219
1.057889
-0.01945
0.96931
0.007246
0.001801689
TR132
1.863638
1.149801
1.658966
-0.05207
1.511256
-0.00755
0.003004418
TR133
1.122373
0.796379
0.972009
-0.03825
0.979137
-0.0404
-0.000144976
TR134
1.266979
0.913982
1.159496
-0.02734
1.099521
-0.00927
0.001219892
TR135
1.331493
1.039332
1.366972
0.009026
1.409523
-0.0038
-0.000865485
TR136
1.233759
0.798056
1.140877
-0.02363
1.076048
-0.00409
0.001318603
TR137
1.332976
0.891758
1.250549
-0.02097
1.262856
-0.02468
-0.000250323
TR138
1.357643
1.112538
1.3564
-0.00032
1.444257
-0.0268
-0.001786997
TR139
0.980238
0.772276
0.814523
-0.04216
0.794994
-0.03627
0.000397201
TR140
1.197035
0.745186
1.022814
-0.04432
0.904708
-0.00873
0.002402257
TR141
1.23393
0.930629
1.071835
-0.04124
0.991137
-0.01692
0.001641385
TR142
1.100314
0.828132
1.042085
-0.01481
1.055184
-0.01876
-0.000266439
TR143
1.193696
0.860483
0.975511
-0.05551
0.841109
-0.015
0.002733726
TR144
1.405515
1.022087
1.338884
-0.01695
1.348534
-0.01986
-0.000196281
TR145
0.951612
0.915484
0.968295
0.004244
1.019414
-0.01116
-0.001039747
TR146
1.045254
0.909158
0.967977
-0.01952
0.933948
-0.00925
0.000692526
TR147
1.02267
0.883177
0.98779
-0.00887
1.072866
-0.03452
-0.001730439
TR148
1.087954
0.680614
0.925261
-0.04139
0.838323
-0.01519
0.001768323
TR149
1.087258
0.835172
0.997776
-0.02277
0.928173
-0.00179
0.00141571
TR150
1.496911
1.036346
1.408879
-0.0224
1.435097
-0.0303
-0.000533276
TR151
1.430685
0.898277
1.383395
-0.01203
1.41507
-0.02158
-0.000644264
TR152
1.055048
0.729682
0.907941
-0.03743
0.887429
-0.03124
0.000417219
TR153
0.816468
0.536162
0.8208
0.001102
0.821251
0.000966
-9.17E-06
TR154
0.66896
0.708135
0.672013
0.000777
0.762641
-0.02654
-0.001843367
TR155
0.935581
0.730399
0.975814
0.010235
1.005047
0.001424
-0.000594599
TR156
1.13879
0.6813
1.084255
-0.01387
0.973619
0.019472
0.00225032
TR157
1.282853
0.861018
1.347786
0.016519
1.416169
-0.00409
-0.001390913
TR158
0.852569
0.720453
0.844077
-0.00216
0.939624
-0.03096
-0.001943425
TR159
1.290137
1.014177
1.243477
-0.01187
1.215561
-0.00346
0.000567809
TR160
0.862379
0.684929
0.760039
-0.02604
0.701833
-0.00849
0.001183907
TR161
0.655587
0.581589
0.734651
0.020115
0.694684
0.032161
0.000812923
TR162
0.904474
0.64735
0.93587
0.007987
0.995279
-0.00992
-0.001208371
TR163
1.065091
0.833478
1.01233
-0.01342
1.052134
-0.02542
-0.000809615
TR164
1.434067
0.899106
1.245939
-0.04051
1.089864
-0.00121
0.002589313
TR165
1.300665
0.763361
1.297691
-0.00076
1.16458
0.039365
0.002707474
TR166
1.042867
0.790171
0.866784
-0.0448
0.743187
-0.00754
0.002513948
TR167
1.14281
0.806125
0.982407
-0.04081
0.855036
-0.00242
0.002590733
TR168
1.283062
0.82272
1.174816
-0.02754
1.089298
-0.00176
0.001739417
TR169
0.906479
0.722989
0.86711
-0.01002
0.77888
0.016578
0.001794595
TR170
1.137723
0.765387
1.03089
-0.02718
0.991497
-0.01531
0.000801248
TR171
1.057791
0.690317
1.001762
-0.01425
0.993507
-0.01177
0.00016791
TR172
1.23225
0.81081
1.124746
-0.02735
1.117382
-0.02513
0.000149777
TR173
1.014553
0.672932
0.938282
-0.0194
0.93704
-0.01903
2.53E-05
TR174
1.28245
0.947068
1.261512
-0.00533
1.320087
-0.02298
-0.001191419
TR175
1.240235
0.841603
1.182151
-0.01478
1.187709
-0.01645
-0.000113058
73
Hafez Barahmeh 2010
TR176
0.737417
0.695895
0.742964
0.001411
0.706694
0.012343
0.000737725
TR177
1.345792
0.884626
1.244058
-0.02588
1.148771
0.002838
0.001938129
TR178
1.116308
0.827761
0.98382
-0.03371
0.890927
-0.00571
0.001889431
TR179
1.149296
0.719576
1.018995
-0.03315
0.709553
0.060119
0.006294012
TR180
0.81076
0.729573
0.836263
0.006488
0.937106
-0.02391
-0.002051125
TR181
0.973432
0.826844
0.834327
-0.03539
0.736855
-0.00601
0.001982566
TR182
1.399677
0.695765
1.241096
-0.03658
1.109283
-0.00148
0.002304564
TR183
0.981941
0.774105
1.033504
0.013118
1.137803
-0.01832
-0.002121425
TR184
1.279635
1.071942
1.201016
-0.02
1.271273
-0.04118
-0.001429003
TR185
1.296094
0.837933
1.15026
-0.0371
1.189875
-0.04904
-0.000805761
TR186
1.098774
0.735942
1.063831
-0.00889
1.070937
-0.01103
-0.000144536
TR187
1.344162
0.993508
1.24117
-0.0262
1.319031
-0.04967
-0.001583692
TR188
1.198588
0.981146
1.138193
-0.01537
1.15764
-0.02123
-0.00039555
TR189
1.223817
0.934532
1.301791
0.019837
1.385626
-0.00543
-0.001705199
TR190
0.869688
0.554567
0.778228
-0.02327
0.759577
-0.01765
0.000379352
TR191
1.134708
0.808049
1.080422
-0.01381
1.129964
-0.02874
-0.001007667
TR192
0.846904
0.844797
0.911737
0.016494
0.88358
0.02498
0.000572697
TR193
0.695252
0.621945
0.645498
-0.01266
0.635375
-0.00961
0.000205916
TR194
1.203105
0.833586
0.996181
-0.05264
0.924305
-0.03098
0.001461958
TR195
0.663294
0.581232
0.665555
0.000575
0.75469
-0.02629
-0.001813004
TR196
1.261925
0.72734
1.160753
-0.02574
1.025029
0.015169
0.002760598
TR197
1.558921
1.079361
1.515093
-0.01114
1.425265
0.015888
0.001824408
TR198
1.267002
0.856451
1.00488
-0.06669
0.965655
-0.05486
0.000797839
TR199
1.056914
0.699944
1.036011
-0.00532
0.995053
0.007027
0.000833088
TR200
1.332885
1.163326
1.246772
-0.02191
1.179142
-0.00152
0.001375596
TR201
1.020728
0.843076
1.080327
0.015162
1.078554
0.015697
3.60E-05
TR202
1.170931
0.666377
1.145849
-0.00638
1.087428
0.011228
0.001188275
TR203
1.172544
0.847193
1.099494
-0.01859
1.102778
-0.01958
-6.68E-05
TR204
1.180185
0.846648
0.998913
-0.04556
0.839997
6.43E-05
0.003070208
TR205
1.104012
0.812716
1.071915
-0.00817
1.158883
-0.03438
-0.001768917
TR206
1.236273
0.827596
1.017826
-0.05558
0.957309
-0.03734
0.001230899
TR207
1.099652
0.774098
0.949595
-0.03818
0.929316
-0.03206
0.000412471
TR208
1.365225
0.849599
1.19482
-0.04335
1.079545
-0.00861
0.00234469
TR209
0.935884
0.73712
0.859158
-0.01952
0.834811
-0.01218
0.000495221
TR210
0.994344
0.719278
0.937782
-0.01439
0.965842
-0.02285
-0.000570722
TR211
1.309729
0.962939
1.130561
-0.04558
1.122207
-0.04306
0.000169932
TR212
1.251508
0.963417
1.145426
-0.02699
1.143149
-0.0263
4.63E-05
TR213
1.090532
0.764887
1.176976
0.021992
1.05653
0.058295
0.002449853
TR214
1.164705
0.767888
1.010223
-0.0393
0.952916
-0.02203
0.001165626
TR215
0.969093
0.756867
0.913575
-0.01412
0.845659
0.006346
0.001381393
TR216
1.173184
0.722588
0.991156
-0.04631
0.888435
-0.01535
0.002089326
TR217
1.428858
0.83567
1.206245
-0.05664
1.262032
-0.07345
-0.001134716
TR218
1.387099
0.812704
1.227654
-0.04056
1.21932
-0.03805
0.000169496
TR219
1.176665
0.815602
1.013386
-0.04154
0.915706
-0.0121
0.0019868
TR220
0.687622
0.605842
0.501403
-0.04738
0.542227
-0.05968
-0.000830364
74
Capital Asset Pricing in Emerging Market: an Empirical Investigation
TR221
1.11124
0.751005
1.112256
0.000258
1.121702
-0.00259
-0.000192142
TR222
1.283151
0.902169
1.136884
-0.03721
1.126854
-0.03419
0.000204014
TR223
1.869197
1.039349
1.579638
-0.07367
1.346638
-0.00344
0.00473919
TR224
1.507831
0.960418
1.353443
-0.03928
1.305097
-0.02471
0.000983354
TR225
1.032957
0.627235
0.936066
-0.02465
0.886339
-0.00966
0.00101145
TR226
0.932631
0.63664
0.772576
-0.04072
0.729107
-0.02762
0.000884152
TR227
1.586062
1.135961
1.577081
-0.00229
1.625049
-0.01674
-0.000975682
TR228
1.30971
0.92695
1.298691
-0.0028
1.179359
0.033164
0.002427197
TR229
1.358004
0.950813
1.365934
0.002017
1.479811
-0.03231
-0.002316254
TR230
1.423823
1.054521
1.381862
-0.01068
1.297457
0.014765
0.001716794
TR231
1.556413
1.023827
1.443263
-0.02879
1.305666
0.012686
0.002798708
TR232
1.070385
0.854285
1.02197
-0.01232
0.911754
0.020903
0.002241782
TR233
1.30026
0.860401
1.219328
-0.02059
1.159839
-0.00266
0.00120999
TR234
0.991329
0.70923
0.876176
-0.0293
0.855859
-0.02317
0.000413253
TR235
0.792836
0.599661
0.598619
-0.04941
0.589193
-0.04657
0.000191731
TR236
1.228058
0.868458
1.210365
-0.0045
1.252919
-0.01733
-0.000865558
TR237
1.350018
0.904349
1.213402
-0.03476
1.219349
-0.03655
-0.00012096
TR238
1.649266
0.976042
1.644591
-0.00119
1.659162
-0.00558
-0.000296363
TR239
1.335538
0.928351
1.274901
-0.01543
1.245368
-0.00653
0.000600694
TR240
0.967516
0.692948
0.788842
-0.04546
0.779271
-0.04257
0.00019467
TR241
1.015237
0.868251
1.031159
0.004051
1.033246
0.003421
-4.25E-05
TR242
1.509067
1.190799
1.549911
0.010391
1.598953
-0.00439
-0.000997491
TR243
1.023796
0.718147
0.892044
-0.03352
0.843973
-0.01903
0.000977764
TR244
1.346559
0.88411
1.310472
-0.00918
1.345033
-0.0196
-0.000702963
TR245
1.144996
0.697968
1.090094
-0.01397
1.070886
-0.00818
0.000390689
TR246
0.861783
0.779948
0.890642
0.007342
1.033172
-0.03562
-0.002899041
TR247
1.353526
0.883593
1.21876
-0.03429
1.17455
-0.02096
0.000899219
TR248
1.044356
0.836294
0.985498
-0.01497
0.988599
-0.01591
-6.31E-05
TR249
0.750609
0.621348
0.590633
-0.0407
0.391696
0.019262
0.004046359
TR250
1.043132
0.765619
0.900941
-0.03618
0.786789
-0.00177
0.002321843
TR251
1.263948
0.781087
1.17957
-0.02147
1.202765
-0.02846
-0.000471793
TR252
1.322883
0.974314
1.191939
-0.03331
1.127392
-0.01386
0.001312878
TR253
1.207884
0.799351
1.065777
-0.03615
1.003236
-0.0173
0.001272068
TR254
0.895765
0.806763
0.806177
-0.02275
0.679025
0.015501
0.002581748
TR255
1.386222
0.885476
1.168312
-0.05544
1.183258
-0.05994
-0.000304002
TR256
1.166623
0.88063
1.136305
-0.00771
1.150214
-0.01191
-0.000282926
TR257
1.4298
1.170071
1.392238
-0.00956
1.419265
-0.0177
-0.000549729
TR258
1.258015
0.81342
1.143453
-0.02915
1.063771
-0.00513
0.001620729
TR259
0.982559
0.683579
0.951108
-0.008
0.980483
-0.01686
-0.000597486
TR260
1.047609
0.840378
0.919992
-0.03247
0.91787
-0.03183
4.32E-05
TR261
1.477389
0.862288
1.339083
-0.03519
1.356384
-0.0404
-0.000351893
TR262
1.53953
1.011386
1.262886
-0.07038
1.152964
-0.03725
0.002235803
TR263
0.831622
0.660538
0.688585
-0.03639
0.651937
-0.02534
0.000745426
1.263354
0.806385
1.118547
-0.03684
1.034544
-0.01152
0.001708617
1.869197 1.190799
1.658966
0.021992 1.659162 0.060119
0.006294012
TR264
Max
75
Hafez Barahmeh 2010
Min
0.537702 0.484897
0.501403
-0.07833 0.391696
-0.14798 -0.007247944
76
Capital Asset Pricing in Emerging Market: an Empirical Investigation
77