Country Risk Components, the Cost of Capital, and
Returns in Emerging Markets
Campbell R. Harvey
a,b
a
Duke University, Durham, NC 27708
b
National Bureau of Economic Research, Cambridge, MA
Abstract
This paper examines the importance of political risk, the financial risk, and economic risk in
portfolio and direct investment decisions. In addition, the components of each of these risk
measures are examined. First, I explore whether any of these measures contain information
about future expected stock returns by conducting trading simulations. Second, I show the
relation between these measures and implied costs of capital based on earnings forecasts. My
results suggest that the country risk measures are correlated future equity returns – but only
in emerging markets. These results are consistent with emerging markets being to some
degree segmented from world capital markets.
Introduction
What is country risk and how should it impact global investment strategies? I explore
the information in Political Risk Services' International Country Risk Guide (ICRG). These
measures include political risk, economic risk and financial risk. The ICRG also reports a
measure of composite risk which is a simple function of the three base indices. In contrast to
previous work, I also explore the components of each of the three categories.
The first part of my analysis investigates the link between these country risk measures
and some more standard measures of risk. I investigate whether there is a correlation
between a country's beta versus the MSCI world index and country risk ratings. While beta is
a standard risk measure for integrated capital markets, many have found the world beta
model inadequate to characterize risk in emerging markets. As an alternative, I also
investigate the relation between the country risk measure and equity volatility. Finally, I
consider return skewness and its relation to country risk.
Next, I explore whether the risk indices contain information about future expected
returns. This analysis is conducted in two ways. First, I form a portfolio of countries with
low risk ratings (more risky) and a portfolio of countries with high risk ratings (less risky). I
find that there is, indeed, information about expected equity returns in these measures.
However, the information is only useful for trading strategies involving emerging markets
The trading strategies are based on historical returns. In the final part of the paper, I
examine the relation between country risk measures and the implied cost of capital. The
implied cost of capital is the discount rate that makes a company’s expected cash flows
(based on analysts forecasts’ of earnings) exactly equal the current stock price. Hence, the
implied cost of capital is based on ex ante rather than historical data. There is a significant
relation between the country risk and the implied cost of capital which is consistent with the
results of the trading strategies. Again, the relation is only significant for emerging markets.
Measuring Country Risk
There are many services that measure country risk. The research of Erb, Harvey, and
Viskanta (1996) details the correlation between the different measures (which includes
Moody’s, Standard and Poors, Institutional Investor and Political Risk Services: International
Country Risk Guide (ICRG). In this chapter, I concentrate on the ICRG data. Indeed, Erb,
Harvey, and Viskanta show that the ICRG data which is available on a monthly frequency is
able to predict changes in the Institutional Investor measure which is available only semi-
annually.
International Country Risk Guide
ICRG compiles monthly data on a variety of political, financial and economic risk
factors to calculate risk indices in each of these categories as well as a composite risk index.
Five financial, 12 political and five economic factors are used. Each factor is assigned a
numerical rating within a specified range. The specified allowable range for each factor
reflects the weight attributed to that factor. A higher score indicates lesser risk.
Political risk assessment scores are based on subjective staff analysis of available
information. Economic risk assessment scores are based upon objective analysis of
quantitative data and financial risk assessment scores are based upon analysis of a mix of
quantitative and qualitative information.
Calculation of the three individual indices is simply a matter of summing up the point
scores for each factor within each risk category. The composite rating is a linear combination
of the three individual indices' point scores. Note that the political risk measure (100 points)
is given twice the weight of financial and economic risk (50 points each). ICRG, as well as
many of the other providers, think of country risk as being composed of two primary
components: ability to pay and willingness to pay. Political risk is associated with a
willingness to pay while financial and economic risks are associated with an ability to pay.
The specific factors taken into account for each risk index are detailed in Table 1. While
previous research has examined the information in the broad categories (i.e. Political,
Economic, and Financial), one of the goals of this chapter is to examine the information in
the components of each of these categories. For example, how important is Law and Order
in the Political Risk versus Investment Profile?
Summary Data Analysis
Variation in risk measures
Our analysis focuses on over 100 countries. I segment the countries into three groups:
All countries, developed countries, and emerging. In the financial analysis, I will reduce the
number of countries by focusing only on those with equity markets.
Figure 1 presents time-series graphs of the equally weighted risk indices for three groups
over the January 1984-July 2004 period. The equally-weighted measures for the developed
countries (Panel A) exhibit remarkably little variation through time. The ICRG financial and
economic measures remain about the same throughout the sample.
1
The analysis for the
emerging countries and all countries (Panels B and C) is different. Generally, all of the risk
rating measures increase over the sample. Obviously, the increase in rating for the all
countries sample is driven by the emerging markets.
Mean reversion of risk ratings
The cross-sectional behavior of the risk measures is explored in Figure 2. I graph the
January 1984 risk level against the change in the risk level up to July 2004. There appears to
be cross-sectional mean reversion in the risk measures. Those countries that began with a
very low risk rating tend to improve. Those countries with a high rating have remained at the
high level or slightly deteriorated.
The cross-sectional behavior of ratings is further explored in Figure 3. In panel A, I
consider the change in the Financial, Economic, Political and Composite ratings for
emerging, developed, countries with equity markets and all countries. It is clear from this
1
Notice a drop in the ICRG Financial rating in September 1997. This is a result of a reorganization of the
components. A smaller drop is evident in the ICRG Composite on the same date.
graph that there has been minimal change in the ratings for developed countries – most of
the improvement has occurred in emerging markets.
The next three panels of Figure 3 examine the components of each of three risk
measures. In Panel B, it is evident that most of the improvement in the financial rating in
emerging countries versus developed countries is due to improved exchange rate stability
and more favorable debt service ratios. Panel C shows that the relative improvement over
developed countries for the economic ratings is being driven by improved capital account as
a percentage of GDP, improved budget balances, reduced inflation and more robust GDP
growth. Panel D shows significant gains in democratic accountability, reduced external
conflict and a sharp improvement in government stability in the political risk category.
Comovement of risk ratings
Table 2A details the correlation of the various risk measures. The upper triangle of the
matrix reports the correlation based on changes in rating and the lower triangle reports the
correlation of the levels. The correlations are calculated by staking all country observations
together.
The correlations are not as high as one might expect. Obviously, the correlation
between the composite and the political rating is the highest because, by definition, the
political rating is 50% of the composite. The highest cross-correlation of the levels of the
three ICRG components is 80%. The correlations for the changes in risk levels are all very
small.
Table 2A also examines the components of each risk measure. Similar to the aggregated
measures, the subcomponents show high correlations in levels and low correlations when
examined as components.
Table 2B shows that stacking all country observations produces higher correlations
compared to averaging correlations across the different countries. However, the flavor of the
results is unchanged.
Persistence of risk ratings
Table 3 shows the degree of persistence in the log changes in the risk measures and the
subcomponents. I report the average autocorrelations. I present the results by all, developed
and emerging countries. In addition, I report the number of countries with autocorrelations
that are significantly above or below zero.
For the composite, economic, political, and financial measures, there is very little
evidence of persistence in the developed markets. Of the 26 countries, for example, only two
show significant autocorrelation in the changes in the Political Risk ratings. The emerging
markets present a similar story. Of the 119 countries, only 6 show significant changes in the
Political Risk ratings – about what one would expect by random chance.
While changes in the Political, Economic and Financial ratings are generally
unpredictable, the story changes when the components are examined. Many of these
components are quite stable. A long string of zeros often induces significant
autocorrelations. It is best to interpret this as persistence in the component risk levels.
Risk Ratings and Returns
Risk ratings and price moments
Table 4 provides a correlation analysis of the ratings with mean returns, volatility, beta
and skewness. In this table, I only examine countries with equity markets. First, I examine
the beta which is calculated against the Morgan Stanley Capital International World Index.
The correlation of the composite risk measure and beta is positive and is 0.16 for in the all
country sample. In addition, the positive correlation is driven by the emerging markets in the
sample. The sign of the correlation is exactly the opposite of what one would expect (low
rated countries which are presumably risky have the lowest beta risk). Figure 4 which graphs
the betas against the average risk measures. This positive relation is largely due to the fact
that a number emerging markets have very low betas with respect to the world market
portfolio [see Harvey (1995)]. Panels A-D show the developed markets. The relation
between betas and ratings is flat for all but the two countries with the lowest ratings. Panels
E-H show the emerging markets. While the relation is weak, some of the lowest rated
countries have lower betas. The picture in Figure 4 contrasts with a similar graph in Erb,
Harvey and Viskanta (1996) which showed a much sharper positive relation between beta
and rating. The reason is simple. Over the past 10 years, these emerging markets have
become more integrated with world capital markets. With increased integration, their betas
with respect to the world tend to increase [see Bekaert and Harvey (2000)] and hence flatten
out the slopes in Panels E-H.
Figure 5 shows that there is a sharp negative correlation between volatility and the risk
measures. This closely squares with intuition. The lowest (highest) rated countries have the
highest (lowest) equity return volatility. This volatility is robust across all risk measures. We
observe a negative relation for developed markets in Panels A-D and emerging markets in
Panels E-H.
Figure 6 explores the relation between return skewness and risk ratings. High risk
countries might experience big upside or downside risk that manifests itself in skewness. I
find that there is generally a negative relation between the risk ratings and skew. The lowest
rated countries have the most potential for a big positive surprise. Interestingly, this relation
is robust across both developed (Panels A-D) and emerging markets (Panels E-H). However,
the relation with skewness presents somewhat of a puzzle. Markets with positive skew
should have low expected returns -- investors like positive skew and should bid up prices
thereby lowering expected returns. But this is not what we observe. The lower rated
countries have both higher expected returns and positive skewness. Nevertheless, there are
two important caveats. First, the negative relation in Panels A-D for the developed countries
is influenced by a few lower rated countries. Second, the relation in Panels E-H for the
emerging markets while negative is only weakly negative.
What type of risk is priced?
Table 4 suggests that there is a relation between average return and average rating. One
way to analyze this relation is to form a portfolio strategy based on ratings changes. One
version of this strategy is analyzed in Diamonte, Liew and Stevens (1996). They form two
portfolios: upgrade and downgrade based on the ICRG political risk measure. Importantly,
their strategy is ex post - rather than ex ante. That is, their strategy is only investable if you
knew in advance what next month's rating would be.
