Total Sample Of Emerging Markets example essay topic
Harvey (1995) points out that the International Finance Corporation (IFC) back filled some of the index data resulting in a survivorship bias in the average returns. Second, the countries that are currently chosen by the IFC are the ones that have a proven track record. This selection of winners induces another type of selection bias. Third, Goetz mann and J orion (1996) detail a re-emerging market bias. Some markets, like Argentina, have a long history beginning in the last half of the 19th century. At one point in the 1920's, Argentina's market capitalization exceeded that of the U.K. However, this market submerged.
To sample returns from 1976 (as the IFC does), only measures the 're-emergence' period. A longer horizon mean, in this case, would be lower than the one calculated from 1976. This insight is consistent with the out-of-sample portfolio simulations carried out by Harvey (1993) indicating that the performance of the dynamic strategy was affect by the initial five years. Fourth, exposure as measured by the IFC is not necessarily attainable for world investor's [see Bekaert and Uri as (1996) ].
Second, we have learned that the emerging market returns are more predictable than developed market returns. Harvey (1995) details much higher explanatory power for emerging equity markets than developed market returns. The sources of this predictability could be time-varying risk exposures and / or time-varying risk premiums, such as in Ferson and Harvey's (1991, 1993) study of U.S. and international markets. The predictability could also be induced by fundamental inefficiencies. In many countries, the predictability is of a remarkably simple form: autocorrelation.
For example, Harvey (1995) details 0.25 autocorrelation coefficient for Mexico in a sample that ends in June 1992. An investor who followed a strategy based on autocorrelation in this country would have lost 35% like everyone else in December 1994. However, the investor would have been completely out of the market in the next three months (or short if possible). Momentum appears to be important for many of these markets. Third, we have learned that the structure of the returns distribution is potentially unstable. Ghysels and Garcia (1996) reject the structural stability of the prediction regressions presented in Harvey (1995).
These regressions allow for the influence of both local and world information. Bekaert and Harvey (1995, 1996) present a model which explains the results of Ghysels and Garcia. The Bekaert and Harvey model allows for the relative influence of local and world information to change through time. They hypothesize that as a market becomes more 'integrated' into world capital markets, the world information becomes relatively more important. Bekaert and Harvey (1996) find that the changing relative importance of world information also influences volatility. Fourth, the Bekaert and Harvey (1996) framework suggests the increasing influence of world factors on emerging expected returns will manifest itself in increased correlation with developed market benchmarks.
The goal of this paper is to explore three aspects of the emerging markets data. First, we examine the behavioral characteristics beyond the volatility - the skewness and kurtosis. Second, the paper explores the relation between risk variables and expected returns. Harvey (1995) and Bekaert (1995) find that higher betas (from a capital asset pricing framework) are associated with lower expected returns. This is the opposite from what we would expect from theory, however, it is consistent with these markets being segmented. That is, the countries with the higher betas are the ones that are more likely integrated, hence have lower expected returns relative to the segmented countries.
Third, we examine the time-varying correlation of these markets with developed markets. Sol nik and Long in (1994) and Erb, Harvey and Viskanta (1995) detail how correlations change through time in developed markets. Harvey (1995) and Bekaert and Harvey (1996) show some evidence that correlations are changing in emerging markets. Finally, we examine what is important for explaining both the cross-section of expected returns and volatility in emerging markets. Following Erb, Harvey and Viskanta (1996), we try to link political, economic, and financial risk, as well as, a number of fundamental attributes to explain the cross-sectional behavior of emerging market returns. 2.
Distribution of Emerging Market Returns 2.1 Which emerging market benchmarks should be used? The two main sources of emerging market benchmarks are the International Finance Corporation (IFC) and Morgan Stanley Capital International (MSCI). Both provide country benchmark indices which are based on a value weighted portfolio of a subset of stocks which account for a substantial amount of the market capitalization within each emerging market. The IFC produces two types of indices: Global (IFCG) and Invertible (IFCI). For nine countries, data exists back to 1976. Currently, the IFC provides data on 27 countries.
MSCI also produces both Emerging Markets Global (EMG) and Emerging Markets Free indices (EMF) which resembles the IFCI. Our paper focus ses on the global indices. Part of the interest in studying emerging markets is the impact capital market liberalizations have on the returns. Hence, we study markets before and after they are accessible by international investors. IFC and MSCI use a different hierarchical process in the company selection for the country indices. MSCI follows the same technique that it uses in its popular developed country indices.
First, the market is analyzed from the perspective of capitalization and industry categories. Next, a target of 60% coverage of the total capitalization of each market, with industry weightings approximating the total market's weightings is established. Finally, companies are selected based on liquidity, float, and cross-ownership to fulfil these goals. In contrast, the IFC's order of preference is: size, liquidity and industry. The IFC primarily targets the largest and most actively traded stocks in each market, with a goal of 60% of total market capitalization at the end of each year.
As a second objective, the index targets 60% of the trading volume during the year. Industry is of tertiary priority. Although there is some hierarchical differences in the structure of construction, there is little difference in the behavior of the IFCG and the EMG. Table 1 details the difference between the IFCG and the EMG returns over identical samples for each index.
Of the 22 countries where there is MSCI and IFC data, the returns indices have greater than 94% correlation. The volatility differences are quite small - as is the tracking error of the two indices. The only country where substantial deviations occur is Argentina. The IFC index produced a 10.6% lower average return and an 21.1% lower volatility. For this country, the correlation between the IFC index and the MSCI index is only 76%. However, much of the tracking error is due to 1988-1989 data.
When we redo the comparison for Argentina beginning in January 1990, the tracking error drops from 61.9% to 10.6%. The correlation increases from 76% to 99%. There is no difference in the mean returns and little difference in the. Hence, even for Argentina, there is does not appear to be a substantive difference between the MSCI and IFC indices. The IFC family of indices presents the longest history and, as a result, we choose to focus on the IFC. In addition, we study total market returns measured in U.S. dollars.
The local currency returns are not, in general, available to international investors. Furthermore, hedged returns are not available either. Table 2 presents the total sample of emerging markets followed by the IFC and some summary measures of capitalization (in U.S. dollars) along with the number of countries in each index and the weight in the IFC Composite..