The Influence of Country RiskPremiums on Stock Market ReturnsA global analysis on the relationship between country risk and

stock market indexes

Michelle Steiger

Presentation for Free Exchange of Ideas Over Lunch, UFM

Based on the thesis for WHU-Otto Beisheim School of Management

MSc Finance, Class 2016

Abstract

This thesis investigates the relationship between country risk premiums and stock

market returns of 41 countries using a fixed effects panel data model for the period of

January 1984 to June 2015.

Data is measured monthly as the logarithmic returns of the stock market indexes and

the changes in the ICRG Composite, Political, Financial, and Economic Indexes.

Additionally, the Political, Economic, and Financial indexes are broken down into their

components to understand their individual influence on the dependent variable.

Finally, the results are studied by separating developed and developing countries to

understand whether markets in different stages of development behave different

towards influential variables.

This thesis aims to answer the research question on whether risk premiums influence stock market

returns.

2

Agenda

Risk & Country Risk

ICRG

Theoretical Framework

Data & Methodology

Analysis and Findings

Conclusion

3

RISK AND COUNTRY RISK

What is Risk?

Despite no single definition of risk, academics agree that risk entails uncertainty and consequences.

� Despite the vast amount of literature on the subject, there is still no unanimity among academics on

the definition of risk, partly because different disciplines have their own definition of risk.

� However, each discipline can agree that risk has two characteristics:

1. Risk is related to uncertainty

2. Risk has consequences

� Recognizing that risk has consequences links risk directly to objectives. This is crucial for the

process of risk management and to understand attitudes (which drive objectives).

� Is risk defined as a negative outcome? Academics lean towards no.

� In finance, risk is defined in terms of variability of actual returns on investment around an expected

return.

5

Country Risk

Two Basic Types of RiskTwo Basic Types of Risk

Systematic Risk

(‘Market Risk’)

• Risk inherent to the entire market

• Cannot be mitigated through

diversification

Un-systematic Risk

(‘Specific Risk’)

• Specific to one or a very small

number of assets

• Can be mitigated through

diversification

“country risk is considered within the context of systematic risks that stem from

economic, political, [and] financial conditions and that affect all the units available in the

economy simultaneously but to different degrees and that occur beyond the control of

investors” (Kara and Karabiyik, 2015, pp. 226).

Economic

Risk

Economic

Risk

Political

Risk

Political

Risk

Financial

Risk

Financial

Risk

Country

Risk

Country

Risk

Country Risk

6

Division of the Literature on Country

Risk

This division is from a historical perspective. Papers try to explain the causes of the crises ex post.

i. Political Crises (60s and 70s): papers focused on MNCs’ exposure to political risk.

Specifically, the influence of governments on companies doing business abroad.

ii. Debt Crises (80s): emerged with the international debt crises in several emerging countries

and focused on the creditworthiness assessment of these countries

iii. Financial Crises (90s): these followed from the Mexican Crisis (1994) and the Asian

meltdown (1997).

Literature on country risk can be divided in terms of crises, source, or type of investment.

Crises

This division is in terms of the source of the risk. Authors can have a more narrow or broad

perspective as to the origins of country risk.

i. Narrow: these authors suggest that country risk arises because of adverse governmental or

sovereign actions that interfere with business operations.

ii. Broad: these authors not only look at governmental sources of risk, but also at any other

cause that block efficient functioning of foreign organizations abroad. This stream of literature

refers to the impact of business conditions by environmental instability.

Source

This division is made by the type of investment made by the foreign firm in the host country.

i. Foreign Direct Investment: these authors study how country risk can affect FDI, or how it can

affect industry-specific investments.

ii. Commercial Bank Loans: these authors gained momentum during the 80s. They focused on

addressing the issue of external debt servicing and tried to measure probabilities of default.

iii. Portfolio Investment: these authors focus on how country risk influences international

portfolio investment, extending theories, such as the CAPM, to an international framework.

Type of

Investment

7

Literature on Country Risk and Stock Market

ICRG, FR,& ER, contain considerable information on SM returns.In developed markets, ER & FR can predict the cross section of expected returns, in emerging markets, PR has some significance.

Erb, C. B., Harvey,

C. R., & Viskanta,

T. E. (1995)

FindingsAuthor(s)Topic examined

The relationship between

country risk measurements

and future expected stock

returns

Approach

Time series and cross

sectional analysis

There is a short-term and long-term causality

from the risk premiums to the stock prices �

an increase in risk leads to a decrease in stock

prices.

Kara, E. &

Karabiyik, L.

(2015)

The effects of country risk

premiums on stock prices in

Borsa Istanbul for the period

of 1999 to 2013

Johansen Cointegration Test

and the Vector Error

Correction Model

PR, FR, & ER all negatively affect stock

returns in Turkey � an increase in risk leads

to a decrease in stock prices.

ERKOÇAK et al.

(2015)

The effects of changes in

country risk premiums on

stock prices in Borsa Istanbul.

Focus on the ROE for 12

commercial banks listed in

the BIST, using a panel data

approach

Short Term: PR & FR have a significant and negative impact on stock market movements. Long Term: all three factors impact stock prices negatively.

Sari et al.

(2012)

The short term and long term

effects of changes in country

risk premiums on stock prices

in Borsa Istanbul.

Autoregressive distributed lag

(ADL) approach

Local PR, ER, & FR significantly determine stock market return volatility and predictability for these markets, and have more predictability powers than global factors do.

Hassan et al.

(2003)

The impact of country risk on stock market return volatility and predictability in the context of the Middle East and Africa (MEAF).

They analyze 10 stock

markets for the period of 1984

to 1999 by using a GARCH

(1,1) model.

Political and Financial Risks seem to have

asymmetric relationships and impact over the

BRIC’s stock markets, whereas the Economic

Risk does not.

Mensi et al.

(2015)

The relationship between stock market return volatility and country risk premiums for BRIC countries.

Dynamic panel threshold

model to allow for a more

interactive relationship

between the variables.8

Importance of Studying Country Risk“With the end of Bretton Woods in 1971, and since the beginning of the 1990s, the world economy has been characterized

by globalization. Most financial markets are fully deregulated, capital flows freely circulate around the world, firms are

internationally exposed, and national economies are increasingly interlinked. Economic integration translates into a higher

sensitivity to foreign events. International trade is more and more crucial for companies and countries alike” (Bouchet et

al., 2003).

� Globalization and the liberalization of financial markets have left firms and individuals more vulnerable to

foreign events.

� The interconnection between national economies has caused political, economic, or other internal variations

in one country to impact countries far beyond its borders; there are always shocks of this nature happening

somewhere in the world with the potential to spread.

� These developments, paired with advances in communication technologies, increases in sophisticated and

complex financial instruments, and the rise in involvement of better informed institutional investors has

made it more important than ever for firms and individuals to keep informed of what goes on beyond their

borders and to find ways of dealing with and try to mitigate this uncertainty.

• Investors have created global portfolios, reducing

risk through diversification, but exposing themselves

to non-domestic political and economic risks.

• Investors in domestic markets are also exposed to

country risk if the companies that they invest in have

interests abroad.

Investors in Financial Investors in Financial Markets

• Geographical expansion has become necessary to

remain competitive.

• The growth of the markets to which they expand to,

or that they invest in, has become crucial for their

success.

• CRA must be incorporated from a group perspective,

and firms must create strategies based on how their

foreign and domestic operations fit together

Large Corporations

• Government actions have direct implications on

country risk.

• A country that is perceived as more risky will have

less FDI and thus, lower economic growth.

• Economic stagnation and economic crises could give

rise to economic turmoil, which leads back to an

increase in country risk

Governments

Most exposed agents to country risk:

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Difficulties on defining a theory of countryrisk

Even though the subject has been and continues to be studied profusely, a comprehensive theory of

country risk has yet to be devised for several reasons.

1. Country Risk is composed by a diversity of sources, which have a complex interaction among each

other.

2. There are multiple social sciences involved in the study of country risk and its components.

3. Each wave of crises studied seems unique given the context and circumstances of the countries

where they occur.

4. The types of investment instruments involved during studied crises also have different effects on

countries and affect them to different degrees, complicating an ex-post evaluation of the topic even

more.

Given these difficulties for defining a theory of country risk, the importance of Country Risk Assessment

(CRA) is now more understandable than ever and its potential is observed in the growing number of

country risk rating agencies.

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Agencies

Agencies providing CRA combine qualitative and quantitative information regarding economic, financial,

and political risk measures and combine these into composite risk ratings.

Some of these agencies are:

� Bank of America World Information Services

� Business Environment Risk Intelligence (BERI), S.A.

� Control Risks Information Services (CRIS)

� Economist Intelligence Unit (EIU)

� Euromoney

� Institutional Investor

� Standard & Poor’s Rating Group (S&P)

� Political Risk Services: International Country Risk Guide (ICRG)

� Moody’s Investors Service

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INTERNATIONAL COUNTRY RISK GUIDE (ICRG)

International Country Risk Guide (I/II)

About the ICRG

� Created in 1980, the ICRG has provided consistent country risk data since 1984 and has formed a

part of the Political Risk Services (PRS) group since 1992.

� It provides monthly ratings for 140 countries and annual ratings for 166 countries.

� The ICRG model mixes statistical analysis with in-depth research, producing information and data for

each particular component of the model.

� The model allows the user to assign a greater weight to a particular risk factor if it is considered more

important for his particular business or investment.

� Howell & Chaddick (1994), for example, find that the indices provided by the PRS group are more

reliable and predict risk better than other major country risk providers.

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International Country Risk Guide (II/II)

Methodology

� Made up of 22 variables that are divided into 3 subcategories of risk: Political, Financial, and

Economic. Each category is made up of different components and also makes up an index in itself.

� Each of these indexes and each of the variables that make up the indexes are assigned a maximum

numerical value, with the highest number of points indicating the lowest potential for risk and vice-

versa. The lowest possible score for each category is zero (the most risky).

� Countries are classified from ‘Very Low Risk’ to ‘Very High Risk’ based on their score. The following

table indicates countries’ classifications based on their score.

� The total points for the three indices are divided by two and added to make up the ICRG Composite

Index, based on 100 points.

Composite and PR FR and ER

Very Low Risk 80 - 100 40 - 50

Low Risk 70 - 79.9 35 - 39.9

Moderate Risk 60 - 69.9 30 - 34.9

High Risk 50 - 59.9 25 - 29.9

Very High Risk 0 - 49.9 0 - 24.914

The Political Risk Index (PR)

� The Political Risk Index aims to assess the political stability of the countries studied by assigning risk

points to a group of political factors.

� The minimum number of points that can be given to each component is zero and the maximum

depends on the component itself.

� The maximum number of points that can be assigned to each component is determined by the

editors of the ICRG based on a series of pre-set questions for each component.

� The Political Risk (PR) Index is based on 100 points and is made up of 12 variables

15

The Economic Risk Index (ER)

� The Economic Risk Index aims to assess the country’s current economic strengths and weaknesses

by assigning risk points to a group of economic factors.

� The minimum number of points that can be given to each component is zero and the maximum

depends on the weight that the component is given; to ensure compatibility, the components are

measured as ratios.

� The Economic Risk (ER) Index is based on 50 points and made up of 5 variables.

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The Financial Risk Index (FR)

� The Financial Risk Index aims to assess a country’s ability to finance its official, commercial, and

trade debt obligations by assigning risk points to a group of financial components.

� The minimum number of points that can be given to each component is zero and the maximum

depends on the weight assigned by a fixed scale.

� The Financial Risk (FR) Index is based on 50 points and is made up of 5 variables.

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THEORETICAL FRAMEWORK

Authors using this approach are mostly interested in

studying the relationship between country risk and the

stock markets itself, and the differences that may arise

depending on the variables of influence or the region

studied.

Authors using this approach include:

� Ramcharran (2003), uses data from the European

Credit Rating to study the effect of political, economic

and credit risk on equity returns and other variables

from 21 emerging markets using panel data.

� Mensi et al. (2015), use dynamic panel threshold

models (a variation of the panel data model) to

investigate the relationship between the ICRG’s three

main indexes (PR, FR, ER) and stock market returns

on BRIC countries.

� Dimic et al. (2015), who use a fixed effects static panel

data model to determine how political risk affects stock

market returns in emerging markets.

The Generalized Autoregressive Conditional

Heteroskedasticity (GARCH) model (or any of its

variations) is widely used by researchers to study the

impact of country risk on returns and volatility.

Authors using this approach include:

� Hassan et al. (2003), use the ICRG to examine the

effects of political, financial, and economic risk

factors on the volatility, predictability, and portfolio

diversification in Middle East and Africa stock

markets.

� Wang and Lin (2009), use the model to study stock

market volatility in Taiwan and examine the

relationship between it and political uncertainty.

� Chau et al. (2014), study the impact of political

uncertainty on stock market volatility in the Middle

East and North Africa (MENA) region.

Theoretical Framework

Many researchers have studied the relationship between country risk and stock market returns. Thereare two main econometric approaches used in the literature to study this relationship.

GARCH Model Panel Data Analysis

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Panel Data (I/III)

� Panel data, also known as longitudinal or cross-sectional time series data, is a dataset in which the

behavior of entities (individuals, countries, companies, etc.) is observed over time.

� It allows the researcher to control for variables that cannot be measured or observed (such as

cultural factors) across states, or for variables that vary over time but not across entities (i.e. national

policies, federal regulations, improvements in the safety of new cars, etc.); hence, it accounts for

individual heterogeneity (Torres-Reyna, 2007, pp. 3).

� Panel Data, therefore, “refers to data for n different entities observed at T different time periods”

(Stock and Watson, 2006, pp. 350).

� In the notation of panel data, it is important to keep track of both the entities and the time periods.

Therefore, two subscripts are used to denote the entity instead of one, like in cross-sectional data;

the subscript i refers to the entity, and the subscript t refers to the time period of the observation.

Hence, Yit denotes variable Y observed for entity i (of n entities) in the time period t (out of T time

periods).

� A generalized panel data model can look like this:

� Yit is the dependent variable, which depends on the characteristics of entity i at time t

� β0 is the constant term.

� Coefficients β1 and β2 times some explanatory variables Xit and Zit, which vary across individual entities and time.

� αi is the hidden unobserved factor (omitted variable) that varies across entities but not across time.

� uit is the idiosyncratic error term.

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Panel Data (II/III)

� Panel data may be balanced or unbalanced.

� A balanced panel data set contains all of the observations, meaning that variables for every entity

and every time period are observed.

� An unbalanced panel is a data set where observations for at least one time period for at least one

entity are missing.

� There are several techniques to analyze panel data. In this thesis, we focus on what are perhaps the

two most commonly used models: the Fixed Effects and the Random Effects regression models.

These models remove omitted variable bias by measuring change within entities across time by

controlling for a number of omitted variables unique to the entity in question.

The variation across entities is assumed to be random

and uncorrelated with the independent variables.

This model allows for time-invariant variables to be

included and are not absorbed by the intercept, as in the

FE model.

Time-invariant characteristics can therefore serve as

explanatory variables, to some degree. Nonetheless,

these characteristics must be specified in the model in

order to avoid omitted variable bias.

Used to analyze the impact of variables that vary over

time. This regression has two major assumptions.

i. Individual entities have characteristics that may

impact or bias the independent or dependent

variables (for example, gender could influence the

opinion towards a certain issue). Assumes a

correlation between the error term of the entity and

the independent variables. Controls for the effects of

time-invariant characteristics (eliminates

endogeneity) so that the net effect of the

independent variables on the dependent variable

can be assessed.

ii. The error term and constant of one entity should not

be correlated with those of another.

Fixed Effects Regression Model Random Effects Regression Model

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Panel Data (III/III)

Three questions can help decide between using a fixed effects (FE) or random effects (RE) regression.

1. What is the nature of the variables that have been omitted from the model?� If the belief is that there are no omitted variables or that they are uncorrelated with the explanatory variables, then the

random effects model is best as it will produce unbiased estimates of the coefficients, use all data available, and

produce the smallest standard errors.

� If the belief is that there are omitted variables and that they are correlated with the explanatory variables, then the

fixed effects model is best, as it controls for omitted variable bias.

2. How much variability is there within subjects?� If there is little to no change over time, a random effects model might be best.

� Fixed effects needs within-entity variability in the variables in order to use the entities as their own controls, therefore,

if this requisite does not hold, the FE model’s standard errors might be too large to tolerate.

3. Is it best to estimate the effects of the time-invariant variables or to control them?� Fixed Effects models do not require the estimation of the effects of the time-invariant variables, as they control them.

� Random Effects will estimate them, but since these variables are not controlled, they might be biased.

Hausman Specification TestStatistical model that tests whether the unique errors (uit) are correlated with the independent variables. The null

hypothesis is that they are not, therefore, that the preferred model is the RE model.

Based on the three preliminary questions, a fixed effects model seemed to be the best alternative for this

specific research paper.22

DATA AND METHODOLOGY

Data Collection & Classification for Stock MarketReturns� Data for Stock Market Returns was taken from the National

Indexes of each county.

• Countries with an active stock market were identified.

• Countries with national indexes were set apart and

data for those indexes were collected.

• Data was obtained from Yahoo Finance services and

websites corresponding stock exchanges.

� Data collected runs monthly from January 1984 to June 2015.

• Time period and interval were chosen to match the

time period of the indexes measured by the ICRG.

• Data for some indexes was not available for every

possible year.

� Data for 41 countries was collected and divided into

developing and developed countries based on the UN World

Economic Situation Prospect 2014.

� Countries and dates were given numbers for the purpose of

Panel Data classification in Stata and for easy identification.

• Dates range from 1 to 378, starting with January 1984

and ending on June 2015.

• Table 4 displays the classification of the countries used

for the empirical analysis.

� Finally, logarithmic monthly returns were calculated and used

as the stock market returns in the model, which served as the

dependent variable.

24

Descriptive Statistics of Stock Market Returns(Developing Countries)

25

The average monthly returns for the stock markets in developing countries is positive in the majority of the

cases. The average return for developing stock markets is 1.03%, with a standard deviation of 13.71%.

Descriptive Statistics of Stock Market Returns(Developed Countries)

26

� The average returns for these countries

concur with the general logic and are lower

(0.38% in average) than those for

developing countries.

� Even though most average returns for

developed countries are also positive, there

is a higher percentage of countries with

average negative returns in stock

markets for developed countries (14%)

than in stock markets for developing

countries (5%).

� Standard deviation for developed countries

is less than half that of developing countries

(6.18%) and the difference between the

minimum and the maximum monthly returns

is much larger for developing markets,

further attesting that they tend to be more

volatile.

� The majority of both developing and

developed markets are negatively skewed.

The kurtoisis for the majority of the markets

is high, especially for developing markets,

and explains that the curve has a high peak,

which is not surprising given the tendency of

the financial returns’ distribution to have

high volatility clustering

Descriptive Statistics for the Political RiskIndex

27

Descriptive Statistics for PR in Developing Countries Descriptive Statistics for PR in Developed Countries

The average PR Index for developing countries (63.5-Moderate) is lower than that for developed countries (81.5-Very

Low). As expected, the average standard deviation for developing markets is higher than that of developed markets

(6.92 and 4.18).

For most emerging markets, the average logarithmic difference for the political risk index is positive, signaling that

these markets are becoming, on average, less risky. The opposite is true in the case of developed markets.

Finally, the average range for the Political Index is much higher for developing (29.15) than for developed (17.55)

markets, indicating a larger volatility of the political components in those countries over the course of the years.

Descriptive Statistics for the Financial RiskIndex

28

Descriptive Statistics for FR in Developing Countries Descriptive Statistics for FR in Developed Countries

Surprisingly, the average Financial Risk (FR) Index for developing countries (35.8) is very close to that of developedcountries (37.8). Both groups’ averages classify as ‘low risk’. Under these ratings, emerging and developed marketshave a similar “ability to finance their obligations”. The number of countries that classify as ‘very low risk’ is almostidentical for both classifications.Developing countries have a higher standard deviation (5.9) than developed countries (2.64), and the range is alsomuch higher (23.55) than in developed markets (15.7), demonstrating that developing markets have a higher volatility inthis area.Positive and close to zero averages for the logarithmic differences in both group of markets indicate that monthlychanges do not tend to be drastic and that they tend to be changes of improvement for the indexes.

Descriptive Statistics for the Economic RiskIndex

29

Descriptive Statistics for ER in Developing Countries Descriptive Statistics for ER in Developed Countries

Out of the three indexes that make up the ICRG Composite, this is the one in which there are less differences amonggroups in terms of standard deviation and range, denoting that emerging and developing economies have similarvolatility. However, despite the similarity among groups, this index is the one that is the most volatile overall. Averagestandard deviation for developing countries is 7.24, higher than the average standard deviation for FR (5.92) andPR(6.92). For developed markets, it is 5.4 versus a low 2.64 for the FR and 4.18 for the PR. The average range for theER for developing countries is 26.74 and for developed countries 22.02.Unlike in the FR Index, the average ratings for developing and developed markets for the ER Index fall under differentcategories. Developing countries show an average Economic Risk Index of 32.72, classifying this group at a‘moderate risk’ level, whereas the Economic Risk Index for developed economies classifies this group at a ‘low risk’level.

Descriptive Statistics for the ICRG Composite Index

30

Descriptive Statistics for ICRG Composite in Developing Countries Descriptive Statistics for ICRG Composite in Developed Countries

The average rating for developing countries is 66.02 (‘Moderate Risk’) and for developed countries 77.94 (‘LowRisk’). The weight of each of the three previous categories is reflected on these results. It is important to remember thatPolitical Risk weighs double that of Financial or Economic Risk.Also, in agreement with the previous results, in average, the ICRG Composite for developing countries is more volatilethan that of developed countries (average sd is 8.88 versus 5.07 and range is 33.85 versus 22.3 respectively).In summary,Differences between developed and developing countries are highlighted most when it comes to politicaldifferences.Economic conditions of a country can be more volatile than political or financialFor the most part, the countries included in this study have tended to become less risky over the past thirty years.

Control Variables

� Control variables are employed in the GLS random effects model to avoid omitted variable bias.

� For the Fixed Effect model these variables are not used, since it is assumed that the model already

controls for these.

� Using control variables in these types of estimations is not the norm, but it is not uncommon.

Ramcharran (2003), Suleman (2013), and Lin and Wang (2004), for example, do not use control

variables in their models, but Mensi (2015), ERKOÇAK et al. (2015) do.

� Six macroeconomic variables that have said to have an influence on stock markets and that have

been used on similar papers were used:

• CPI (monthly change in consumer price index),

• xrusd (monthly logarithmic difference in exchange rate- local currency per US dollar),

• stint (monthly logarithmic difference in the country’s short term interest rate),

• ltint (monthly logarithmic difference in the country’s long term interest rate),

• wti (monthly logarithmic difference in the west texas intermediate index, measuring the dollar price of oil per

barrel)

• gold (monthly logarithmic difference in the price of gold- US dollar per ounce).

� Control variables were taken from several OECD data bases for the monthly periods from January

1984 to June 2015. Data availability, however, was not complete for all countries or time periods.

31

MethodologyThe study examines the relationship between changes in country risk, measured by the logarithmic difference between

monthly measurements of the political, financial, economic, and composite risk ratings from the ICRG and stock market

returns, measured by the logarithmic difference between the monthly stock market indexes collected.

To achieve this, three major econometric models were conducted. Note that all regressions were calculated using robust

standard errors to control for Heteroskedasticity.

This first approach allowed the data

to be treated as a bigger cross-

section to explore the impact of the

different levels of country risk on

stock market returns. This model

assumes no heterogeneity in both

time and cross-sectional dimensions.

Regressions:

Pooled OLS Model Random Effects GLS ModelFixed Effects Regression

Model

Methodology� The study examines the relationship between changes in country risk, measured by the logarithmic difference between

monthly measurements of the political, financial, economic, and composite risk ratings from the ICRG and stock market

returns, measured by the logarithmic difference between the monthly stock market indexes collected.

� To achieve this, three major econometric models were conducted. Note that all regressions were calculated using

robust standard errors to control for Heteroskedasticity.

Data was treated as a bigger cross-

section to explore the impact of the

different levels of country risk on

stock market returns. This model

assumes no heterogeneity in both

time and cross-sectional dimensions.

Regressions:

Pooled OLS Model Random Effects GLS ModelFixed Effects Regression

Model

� A Hausman Test was conducted to determine whether a FE or a RE regression was necessary in the model.

� Two major tests were conducted for the variables: a correlation test for the variables in the second and third

regression for each model to test for collinearity, and unit root tests for all the variables, to test for stationary.� It is important to note that the data was divided into developing and developed countries, and that the methodology was

performed for both data sets. Finally, all tests and regressions were performed using Stata 14 software for

econometrics.

ANALYSIS AND FINDINGS

Analysis and Findings (I/V)Correlations

Correlations for the monthly logarithmic differences between the indexes were performed for the explicatory variables in

the regressions to confirm that there was no collinearity in the models (with the exception of the regression for the

Composite Index, since it is the sole independent variable).

There is a weak positive correlation at most among the independent variables in the case of both the developing and

the developed countries’ data bases. This indicates that the explanatory variables don’t have perfect (or ever strong)

collinearity, and thus, multicollinearity should not be a problem when calculating the model.

Correlations for Developing Countries Data Set Correlations for Developed Countries Data Set

Analysis and Findings (II/IV)Unit Root

� Three different Unit Root Tests were conducted on the stock market index and the four major ICRG indexes to discover

whether a unit root is present in the time series data.

� The conducted tests were the Im-Pesaran-Shin unit root test, and two Fisher-type unit root tests; the augmented

Dickey-Fuller and the Phillips-Perron were conducted on each panel.

� All three tests for the Stock Market (SM) Index show the presence of a unit root (in both the cases of developed and

developing markets), and all of the three tests for the ICRG Composite (COMP) Index, and the PR, FR, and ER

Indexes show stationarity. The presence of unit root in the SM Index confirms the random-walk hypothesis: past

movements of stock markets cannot be used to predict their future.

� Additionally, the results of stationarity for the ICRG Indexes are not surprising, since experts craft the indexes and

assign the ratings based on an established system and under careful considerations; hence, the indexes do not change

unless there is a good reason for them to change.

The Hausman Test

� The regressions were examined under both the Hausman test and the theoretical framework previously exposed to

determine whether FE or RE would fit best for the model.

� Results for the Hausman test show that a RE model is a best fit for the regression. This is intuitively understandable,

since political, economic, and financial factors are very likely to have unique characteristics across countries that

influence stock market returns. However, despite the fact that the control variables have some explanatory power, it

is very unlikely that these unique characteristics influencing the indexes across countries can be entirely found and

measured. Additionally, since data has been taken from different sources (yahoo finance, OECD, ICRG), it is likely that

differences in measurements can affect the results.

� For these reasons, given that the Fixed Effects regression already controls for the omitted variables, it was chosen as

the best fit for the model.

Analysis and Findings (III/V)Findings for Developing Countries � Political Risks have a highly significant influence on stock market

returns (p-value significant at the 1% level). This coincides with the

literature in that PR is the most influential out of the three indexes in

emerging country’s stock markets. A decrease in political risk has a

positive effect on the stock market returns.

� The factors that make up the political risk are the most influential.

� Risk for ‘ethnic tensions’ is significant at a 5% level. It is

observable that as ethnic tensions improve, stock market indexes

improve.

� GDP Growth is significant at a 10% level and it is positively

correlated to stock market returns. A 1% growth in GDP leads to an

almost 2% growth in stock market returns.

� Surprisingly, ‘military in politics’ has a negative coefficient. More

involvement of the military in government is rewarded by the stock

market instead of punished. This could be because this involvement

translates into government spending on military, which at the same

time, increases GDP (increases government spending factor of

GDP).

� Budget Blance as a percentage of GDP is significant at a 10%

level. This could be because the index measures the budget balance

as a percentage of GDP. Therefore, if the GDP increases, the ratio

decreases, but the stock market would increase (even if the change

in the index is negative). Another reason might be because an

increase in government deficit could be because government is

either increasing their spending or decreasing taxes; either way, the

private sector benefits from this deficit, increasing short-term returns

on stock markets.

Analysis and Findings (IV/IV)

� Significant relationship between country risk indexes and stock

market returns.

� Unlike in developing countries, the ICRG Composite Index seems

to be significant for developed markets. A positive change in the

index (a decrease in risk for a country) seems to have a positive

effect on stock market returns (5% level). This makes intuitive

sense, as a country with less inherent risks attracts more

investment and its people and businesses can generally thrive

under the right circumstances.

� PR and ER are not significant for stock market returns. Changes

in FR, however, are significant at a 1% level.

• Political changes do not completely de-stabilize these

countries

• Financial instruments and general involvement from the

population in financial markets are more common

� In the sub-variables, financial factors are the most influential. A

stronger currency, as well as a more healthy liquidity from the

markets will translate into better stock returns.

� Exchange Rate Stability is the most significant variable (at 1%

level), and International Liquidity is significant at a 10% level.

CONCLUSIONS

ConclusionsThe purpose of this thesis was to study the relationship between country risk and stock market

premiums. Through a Fixed Effects panel data regression it was determined that there is in fact a

correlation between country risk and stock market premiums for both developed and developing

markets. The type of relationship, however, varies depending on the market. Developing markets

seem to be influenced strongly by Political Risk components, while developed markets seem to

be influenced by Financial Risk and Overall (Composite) Risk components. In both cases, a

decrease in risk leads to an increased return in the respective stock market.

QUESTIONS?

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