The attractiveness of emerging equity markets is now well-known. Between 1987 and 1993, the proportion of foreign portfolio investment from industrial countries that was directed to emerging markets rose from 0.5 percent to 16 percent (Folkerts-Landau and Ito (1995)). Growth in emerging equity markets has indeed been so strong that market capitalization is larger in relation to GDP in some emerging markets than in many so-called mature markets.1
Despite the rapid growth in emerging equity markets, there has been relatively little published research into the behavior of these markets. Much of the early work in this area did little more than to demonstrate the low correlations between emerging markets and mature markets and to infer the large mean-variance gains from the addition of stocks from these countries into portfolios of industrial country equities. More recently, serious work on these markets has begun, as witnessed by a conference on emerging markets organized by the World Bank in 1993 (see Claessens and Gooptu (1993)). There has also been research in the wake of the Mexican crisis of late 1994 and early 1995 (for example, Folkerts-Landau and Ito (1995) and International Monetary Fund (1995)).2
A widely held perception about emerging equity markets is that price or return indices in these markets are frequently subject to extended deviations from fundamental values with subsequent reversals, as seen in Mexico in 1994-95. In addition, there is a perception that these swings may be due in large part to the growing influence of highly mobile foreign capital, which may have increased volatility in these markets. For example, The Economist (May 13, 1995, p.71) discusses the emerging markets boom of 1993— 94 when “fund managers rushed lemming-like to buy [emerging market stocks].... But, as so often in financial markets, boom was followed shortly by bust. . . . The evidence now suggests that it was investment-fund managers who panicked, in fear of mass redemptions.” And the Financial Times (October 6, 1995, p. S6) suggests that “the creeping globalization of capital flows more or less guarantees that there will be greater volatility in the future—and other Mexicos.”
Similar views have also been expressed by academics and practitioners. For example, Williamson (1993) suggests that foreigners may tend to show herdlike behavior in their investment decisions and that bubbles in asset prices in emerging markets may develop. Gooptu (1993) also suggests that increased volatility may result from herding and rapid switching of portfolios between markets. He notes also that many emerging stock markets suffer from a shortage of good-quality, large-capitalization shares, which results in rapid overheating when domestic and international interest is stimulated by market liberalization. And Howell (1993) suggests with respect to emerging markets that there is an absence of domestic long-term investors, that foreign investors have become the marginal investors, and that the mobility of these investors will result in high price volatility.
This paper examines the evidence for these propositions. To investigate this conventional wisdom, I first test the proposition that the volatility of emerging equity market returns has increased in recent years. While it is true that the volatility of emerging markets is, on average, higher than the volatility of mature markets, I find no evidence to support the proposition that there has been a generalized increase in volatility in emerging markets in recent years. Misperceptions about volatility may therefore be more a function of greater focus on emerging markets: price changes that might previously have gone unnoticed now attract significant attention in the international financial press.
To investigate the possibility of long swings in asset prices, I test for long-horizon predictability of returns in emerging markets, using tests that have so far been applied only to data for mature markets. Of course, a study of long-horizon return predictability in emerging markets is hampered by the relatively short time series available, not to mention the differences in the degree of openness in the various markets and the significant structural changes that have accompanied market liberalization in recent years. While these factors represent an important qualification to the tests in this paper, it should still be possible to gain some understanding of the process by which returns in less-developed markets are generated. The results may also allow inferences on how emerging markets may change as they are further liberalized and become more like mature markets.
These tests for predictability provide evidence that countries that have performed poorly or well in a quarter are likely to continue this performance in subsequent quarters. However, there is evidence of return reversals at horizons of a year or more. In particular, countries that have underperformed in a given “ranking period” tend then to overperform in the subsequent “test period,” and vice versa for initial overperformers. The return differentials in the test period are large, at 10 percent a year or more, although the relatively short data sample makes the statistical significance of these results marginal.
This result is not wholly unexpected, given that similar “winner-loser” reversals have been shown for industrial countries (see Richards (1995 and 1996)). While the magnitude of the reversals is somewhat larger in absolute terms than for industrial countries, it is perhaps smaller in relative terms when the magnitude of the return differentials in the ranking period is considered. Preliminary investigations would suggest that the return differentials cannot easily be attributed to risk, where this is measured in terms of either the variance of returns (the relevant measure of risk if markets are segmented) or the covariance with the world return (the relevant measure if markets are integrated); however, a more sophisticated treatment of this issue might well yield a different conclusion.
Section I of this paper provides a discussion of related previous research. The data used in the study are discussed in Section II. Section III investigates the short-term volatility of emerging markets. Tests for long-horizon return predictability are presented in Section IV. Section V concludes by relating the results of this paper to discussions of the benefits and costs of market liberalization.
I. Previous Research
There are few formal tests of asset pricing models using data from emerging equity markets. Claessens, Dasgupta, and Glen (1995b) use data for individual stocks to conduct cross-sectional tests of the capital asset pricing model (CAPM) in 19 countries and find that the CAPM is typically rejected because of either significant alphas (regression intercepts) or insignificant betas (national-market risk exposures). Furthermore, where other variables, such as company size, are significant, they often show the opposite signs to such effects in industrial country data.
Using data for national market indices, Harvey (1995) tests a simple one-factor international CAPM under the implicit assumption that markets are integrated and obtains betas (world-market risk exposures) that are typically much lower than those found in studies using data for mature markets. In contrast to the implication of the CAPM, alphas are positive for most countries and in some cases suggest very large pricing errors. Furthermore, unlike in studies of mature markets, betas are not found to be a significant explanator of average country returns. This result, along with the significant correlation between average returns and the variance of monthly returns in emerging markets, suggests that these markets were not fully integrated into world capital markets in the period of the study (1976-92).
Indeed, studies testing the extent of integration of emerging markets, including those by Claessens and Rhee (1994) and Errunza, Losq, and Padmanabhan (1992), typically yield results that are consistent with complete or mild segmentation for stocks in most countries. Bekaert and Harvey (1995a) estimate a regime-switching model of expected returns that allows returns to be determined at different times by domestic factors (segmented markets) or by world factors (integrated markets). Their results suggest that certain emerging markets (for example, Greece, Korea, Malaysia, and Taiwan Province of China) have been fairly well integrated into the world market in recent years, while others are much more segmented. In many cases, their model yields results that are consistent with the observed market regulations.
Studies investigating the volatility of returns in emerging markets have yielded mixed conclusions. Kim and Singal (1993), computing estimates of the volatility of domestic currency monthly returns, suggest that there has been no increase in volatility over time, and that volatility has tended to decrease following market liberalizations. However, Levine and Zervos (1995a) suggest that volatility may increase after liberalization. More generally, Bekaert and Harvey (1995b) test a variety of sophisticated models of conditional volatility and find that volatility is difficult to model in these markets. They find evidence, however, that the importance of world factors in emerging market volatility may be increasing, and that volatility tends to decrease following market liberalization.
A broader concept of volatility would, however, allow for the possibility that returns in different periods may be positively autocorrelated, so that price changes in one period are accentuated in subsequent periods and result in long swings in asset prices. Claessens, Dasgupta, and Glen (1995a) examine return behavior in emerging markets and test for autocorrelation and other “anomalies” that have been identified in mature markets. The authors find evidence of positive first-order autocorrelation in monthly returns in 9 of 20 countries. In addition, using variance-ratio tests, they reject the random walk hypothesis of stock prices for 7 out of 20 countries, with test statistics suggesting positive autocorrelation in return horizons of up to four months.
This paper expands on previous tests for volatility of returns by taking a longer-term view of volatility, using monthly data for the period December 1975-September 1995 and weekly data for the period December 1988—September 1995. Three different methodologies are used to estimate volatility, and cross-country average volatility measures are calculated based on volatility estimates for individual countries.
Tests for the predictability of returns are also presented for return horizons of up to three years, in contrast to the shorter-horizon tests of Claessens, Dasgupta, and Glen (1995a). First, tests for stationarity and cointegration of total return indices are conducted, because these properties would imply a very strong form of long-horizon predictability. The second methodology used is the regression approach of Fama and French (1988); this approach has revealed in the case of the United States the presence of positive autocorrelation at horizons of a few quarters and negative autocorrelation at horizons of two years or more. The third methodology is a test of relative return predictability using the winner-loser methodology introduced for portfolios of U.S. stocks by DeBondt and Thaler (1985). Those authors show that at horizons of three to five years there is a tendency for U.S. stocks to undergo reversals in their relative performance: winners and losers in a ranking period tend to undergo return reversals in the following test period.
A pattern of positive autocorrelation at short horizons followed by negative autocorrelation at longer horizons has been identified by Cutler, Poterba, and Summers (1991) in a wide range of asset classes, including equities, currencies, precious metals, real estate, and collectibles. This pattern of “speculative dynamics” might be explained by the existence of fads, sentiment, or some other form of irrationality (for example, Poterba and Summers (1988)). However, other explanations are also possible. Positive autocorrelation and predictability at short horizons might be due to predictability in risk premiums or risk exposures (Harvey (1995)). Negative autocorrelation at longer horizons could also be consistent with infrequent changes in required rates of return, which have immediate effects on asset prices in one direction and an offsetting influence in subsequent periods (Fama and French (1988)).
Evidence of return predictability should, therefore, not be taken as evidence against market efficiency unless there is predictability in risk-adjusted returns. Furthermore, predictability in relative returns—for example, in winner-loser reversals—should not be taken as evidence against market efficiency unless there are no barriers between these markets. As this paper addresses the issues of risk and market access only in passing, its results cannot be used as evidence for or against market efficiency. Instead, the goal of this paper is to characterize the returns process in emerging markets and the manner in which it differs from mature markets.
For the emerging markets, all data used in this paper are from the Emerging Markets Data Base (EMDB) produced by the International Finance Corporation (IFC).3 The IFC includes as “emerging markets” those stock markets in countries or territories with income levels that are classified by the World Bank as low or middle income. There is significant variance in market development across emerging markets: market capitalization and turnover rates range from very low (for example, in Nigeria, which is completely closed to foreign investors) to very high relative to most mature markets (for example, in Chile and Korea). The EMDB includes two countries—Greece and Portugal—that are typically classified as industrial countries. It does not include the stock markets of Hong Kong or Singapore, which are generally regarded as mature markets.
The series used in this paper are the IFC “global” indices, which typically encompass at least 60 percent of total market capitalization in each national market (see International Finance Corporation (1995)). Data for 9 countries are available from December 1975, and additional countries have been added over time, so that the EMDB included 27 countries at the end of 1995.
With the exception of Nigeria (see footnote 6), all countries with at least ten years of monthly data are used in the monthly and quarterly tests. Sixteen markets are included: Argentina, Brazil, Chile, Greece, India, Korea, Mexico, Thailand, and Zimbabwe (all available from December 1975); Jordan (from December 1978); and Colombia, Malaysia, Pakistan, the Philippines, Taiwan Province of China, and Venezuela (all from December 1984). The volatility tests that use weekly data include all 16 markets with weekly data available from the end of 1988: Argentina, Brazil, Chile, Colombia, Greece, India, Jordan, Korea, Malaysia, Mexico, the Philippines, Portugal, Taiwan Province of China, Thailand, Turkey, and Venezuela.
For tests requiring data for mature markets, the Morgan Stanley Capital International (MSCI) indices are used for the following 16 markets: Australia, Austria, Canada, Denmark, France, Germany, Hong Kong, Italy, Japan, the Netherlands, Norway, Spain, Sweden, Switzerland, the United Kingdom, and the United States.
End-quarter data are used in the tests for predictability of long-horizon returns, while the tests for volatility use end-week and end-month data. Returns at these horizons should be largely free of the usual nontrading biases that plague data on daily returns, as well as of problems of asynchronous trading owing to different time zones.4 For all tests, indices for total returns (price plus dividends) are used to capture the total return available to investors. To ensure the cross-country comparability of results, most tests are conducted using returns measured in U.S. dollars, and excess returns are calculated as returns in excess of the U.S. risk-free interest rate. This rate is proxied by the return on the treasury bill index published by Ibbotson Associates, updated for 1994 and 1995 from the IMF’s International Financial Statistics (IFS).
Because the returns are all measured in U.S. dollars, the return behavior identified in this paper includes the effect of both equity and exchange market behavior. Equity market returns tend, however, to be far more volatile than foreign exchange returns, so it is likely that the results identified in this paper predominantly reflect equity market behavior.5
However, a more important point on the exchange rate issue relates to the existence of capital controls and restrictions on foreign investment. The U.S. dollar exchange rates used by the IFC are intended to represent market rates at which a foreign investor can trade; where there is no market rate, an official rate is used (International Finance Corporation (1995)).6 However, in periods of restrictions on foreign investment, capital controls, and administered exchange rates, U.S. dollar returns may not be entirely meaningful.
An alternative would be to conduct the analysis in terms of domestic currency excess returns. This is impossible, however, because of the absence of reliable short-term interest rate data for most countries in the study. Another alternative is to conduct the analysis in terms of real domestic currency returns (that is, returns in excess of domestic consumer price inflation). A problem with this alternative is that price index data are typically monthly average or midmonth data while the stock return data are end of month. Despite this timing difference, most of the results in this paper have been replicated using real domestic currency returns for the sample period to June 1995 (more recent consumer price index data were not available for many countries).7 This replication points to the robustness of the results using data on U.S. dollar returns and indicates that the return behavior identified in this paper is primarily that of equity markets rather than currency markets.
III. Assessing the Volatility of Returns in Emerging Equity Markets
To explore the conventional wisdom that emerging equity market returns have become more volatile, I use three different methodologies and two data sets to estimate volatility. The first data set includes the 9 emerging markets for which monthly data are available for the period December 1975-September 1995, while the second consists of the 16 emerging markets with weekly data available for the period from the end of 1988 to the end of September 1995. In each case, log-differenced returns are used.
For the monthly data, three techniques are used. First, volatility is proxied by the rolling 12-month standard deviation of monthly excess returns. Second, a two-step regression technique based on Schwert (1989) is used to estimate the conditional standard deviation of excess returns. This method, which is similar to an autoregressive conditional heteroscedasticity (ARCH) approach, has been widely used to infer volatilities where only low-frequency data are available (see also Kim and Singal (1993) and Levine and Zervos (1995a)). In the first step, expected returns are obtained from a regression of monthly returns on 12 lagged returns and monthly seasonal dummy variables.8 In the second step, the absolute value of the residual from the first equation (the unexpected return) is regressed on 12 lagged values and monthly seasonal dummies. When multiplied by (2/π)-0.5, the fitted values from the second regression can be used as a proxy for the conditional standard deviation (see Schwert (1989) for further details). To reduce the noise in this measure and to ensure consistency with the first method, the figures showing this estimate of volatility use a rolling 12-month average of fitted values.
A third, more ad hoc, measure of volatility is given by the relative frequency of extreme movements in equity prices. To produce this measure, I calculate the standard deviation of returns in each country over the entire sample period and identify as extreme outcomes all months in which returns are more than two standard deviations from the mean return for that country.9 For each country, a rolling 12-month average is used to represent the percentage frequency (or probability) of extreme outcomes. The time profile of these probabilities can be used to infer whether extreme return outcomes have become more frequent over time.
For the weekly data, the same three methods are used to proxy volatility. In this case, the rolling average volatility measures are constructed over 13-week periods (corresponding to one quarter). For the two-step Schwert methodology, 13 lagged values are used in each regression, without seasonal dummies. Weekly returns are measured in simple (rather than excess) U.S. dollar terms.
For both monthly and weekly data, volatility measures are estimated separately for each market. A measure of average volatility for all emerging markets is then constructed as the weighted average of individual volatilities, with weights based on average shares in total market capitalization.10 The use of an average measure will enable judgments as to whether return volatility has changed on average in emerging markets.
Estimates of the first two measures, the rolling standard deviation of monthly returns and the Schwert measure, are shown in Figures 1 and 2 for nine emerging markets (Argentina, Brazil, Chile, Greece, India, Korea, Mexico, Thailand, and Zimbabwe) and two groups of eight mature markets, grouped by average market capitalization over the 1975-94 period. While there is no necessary relationship between total market capitalization and capital market development, the larger mature markets (the United States, Japan, the United Kingdom, Canada, Germany, Switzerland, France, and Australia) tended to be more liquid and more open than the smaller mature markets (the Netherlands, Italy, Hong Kong, Spain, Sweden, Denmark, Norway, and Austria) over this period.11 Two series for the emerging markets are shown in each figure, one for U.S. dollar excess returns and the other for real domestic currency returns. As the series show very similar movements, the conclusions about volatility are not sensitive to the currency unit used for the emerging markets.
While the two measures fluctuate significantly, they provide a very similar picture of return volatility. It is apparent from both figures that the largest industrial country markets have consistently had lower volatility than the smaller industrial country markets, and that the emerging markets on average have had the highest volatility. The higher volatility of emerging markets is not unexpected and will be discussed further below.
In the case of the industrial countries, there appears to be no long-run trend in volatility. Recent peaks in volatility correspond to the October 1987 market crash and the August 1990 invasion of Kuwait and the subsequent war. For the emerging markets, also, there is no obvious long-run trend. There are two similar spikes in emerging market volatility, with a peak being reached around the year ended February 1991.12 Since then, volatility has been quite low by historical standards, even including the 1994–95 Mexican crisis. According to the raw Schwert estimates, the period 1992–95, which saw foreign institutional investors playing a more significant role in emerging markets (see, for example, International Monetary Fund (1995)), has been characterized by volatility that is marginally lower than the remainder of the sample period. The point estimate for the decline in this period is 6 percent, although the statistical significance of this decline is very marginal.13 The equivalent declines for the large and small industrial countries are 6 percent and 3 percent, respectively. There is thus little to suggest that emerging market volatility has increased in either absolute or relative terms in the 1992–95 period.
Figure 1.Average Standard Deviation of Monthly Returns in Mature and Emerging Markets
Note: The nine emerging markets are Argentina, Brazil, Chile, Greece, India, Korea, Mexico, Thailand, and Zimbabwe. The eight large markets are Australia, Canada, France, Germany, Japan, Switzerland, the United Kingdom and the United States. The eight small, mature markets are Austria, Denmark, Hong Kong, Italy, the Netherlands, Norway, Spain, and Sweden.
Figure 2.Average Conditional Standard Deviation of Monthly Returns in Mature and Emerging Markets
Note: For listing of market groups, see Figure 1.
Figure 3 shows the results of the third technique for estimating volatility, the probability over time of extreme outcomes in monthly returns weighted across the same nine emerging markets included in Figures 1 and 2. First, it might be noted that the average probability of extreme return outcomes fluctuates around 5 percent, as would be expected based on the definition of an extreme outcome as one that is more than two standard deviations away from the mean return. With regard to the pattern over time, these data provide a similar picture to Figures 1 and 2: volatility increased during the 1987 crash and the 1991 Middle East War and has since subsided. Estimates for the industrial countries (not shown) display similar patterns.
For weekly returns in the 16 countries with weekly data, the first two measures of volatility—the conventional standard deviation and the Schwert estimate—are shown in Figure 4. These series show an increase in volatility at the time of the 1994-95 Mexican crisis, and a larger spike earlier in 1994, following the tightening in U.S. monetary policy. Nonetheless, the volatility in these episodes is estimated to be lower than for much of the first half of the sample period. The average probability of extreme outcomes for weekly returns is shown in Figure 5. This picture is similar to that in Figure 4, as volatility was higher than normal in the two recent episodes but still somewhat below the earlier peak.
Figure 3.Average Probability of Extreme Outcomes for Monthly Returns in Nine Emerging Markets
Note: Extreme outcomes are defined as months in which the log-differenced return is more than two standard deviations from its average. For listing of market groups, see Figure 1.
Figure 4.Average Standard Deviation of Weekly Returns in 16 Emerging Markets
Note: The 16 emerging markets are Argentina, Brazil, Chile, Colombia, Greece, India, Jordan, Korea, Malaysia, Mexico, the Philippines, Portugal, Taiwan Province of China, Thailand, Turkey, and Venezuela.
Finally, because regional patterns may vary, estimates for the rolling standard deviation measure of volatility are shown in Figure 6 for the six Latin American countries as a group and for the ten other emerging markets with weekly data. These series suggest that, although volatility can differ quite significantly across emerging markets, there is no clear trend for increasing volatility in either group of markets. Indeed, the raw Schwert estimate of volatility suggests a decline between the 1989–91 and 1992–95 periods of 11 percent in the Latin American countries and of 24 percent in the other ten countries. Both these changes are strongly statistically significant.14 Indeed, of the 16 countries, a statistically significant decrease in volatility was recorded in 8 countries, with statistically significant increases in volatility in only 2 countries (Colombia and—not surprisingly—Mexico). The remaining countries show no significant change.15
The conclusion that the volatility of weekly returns has tended to fall may actually be stronger than suggested by this evidence. In particular, non-trading biases cause national market indices to be excessively smooth, and measured, short-term (that is, daily and probably weekly) return volatility to be underestimated relative to the (unobservable) underlying volatility. However, to the extent that turnover has increased in recent years, this bias should have fallen, and, other things being equal, returns should appear to be more volatile. If measured return volatility has fallen, underlying volatility may actually have fallen by more. In any event, the results for weekly returns confirm the analysis of the monthly volatility estimates: on average, there seems to be no tendency for an increase in volatility in emerging equity markets, even when the “dice are loaded” by conducting such tests soon after a period of known high volatility.
Figure 5.Probability of Extreme Return Outcomes for Weekly Returns in 16 Emerging Markets
Note: Extreme outcomes are defined as weeks in which the log-differenced return is more than two standard deviations from its average. For listing of emerging markets, see Figure 4.
Figure 6.Average Standard Deviation of Weekly Returns in Emerging Markets
Note: The six Latin American markets are Argentina, Brazil, Chile, Colombia, Mexico, and Venezuela. The ten other markets are Greece, India, Jordan, Korea, Malaysia, the Philippines, Portugal, Taiwan Province of China, Thailand, and Turkey.
The evidence of the previous subsection suggests that the volatility of returns in most emerging equity markets may recently have fallen rather than increased, although the short postliberalization sample makes this conclusion somewhat tentative.16 However, other research supports the proposition that the participation of foreigners need not lead to an increase in the volatility of returns. Bekaert (1995) finds that volatility, while unrelated to any measure of market openness, may be negatively correlated with a measure of market integration. He concludes that the fears that opening up a market will lead to greater market volatility may therefore be mistaken. Kim and Singal (1993) study the effect of the liberalization of equity markets in 20 countries and conclude that liberalizations are typically accompanied by increases in stock prices and decreases in the volatility of monthly stock price changes. Bekaert and Harvey (1995b) also find that, following market liberalization, volatility is unchanged or lower in 13 of 17 countries. Finally, Tesar and Werner (1995) find no evidence that U.S. investment activity contributes to volatility in equity returns in emerging markets, while Reinhart and Reinhart (1994) find that no generalized increase in volatility in emerging markets has accompanied the increase in equity inflows into these markets.17
If volatility has actually fallen, what explains the perception of increasing volatility? One factor may be that volatility is still higher than in mature markets, so that portfolio managers shifting for the first time into emerging markets may—depending upon covariances—experience an increase in the volatility of their portfolios. Similarly, if market capitalization is growing faster in emerging markets than in mature markets, the volatility of the world portfolio may be increasing. Most likely, however, perceptions of increased volatility may be due more to greater attention paid to price changes in these markets by the international financial press—as a result of the buildup in holdings by investors from industrial countries—than to actual increases in volatility.
Another factor could be that the volatility of the return on a portfolio of emerging market equities may increase over time even though the volatility of individual emerging markets may on average be falling. Indeed, when the volatility measures used above are calculated for the return on a portfolio of emerging markets, some of them suggest that volatility might have increased modestly. This outcome is due to a tendency for correlations between countries to increase, perhaps because of the growing integration of emerging market economies into the world economy.18
While large numbers of highly mobile foreign investors may at times contribute to large changes in asset prices, it is also clear that emerging markets were highly volatile before the arrival of these investors. If this greater volatility in returns is due to the variability of dividends or earnings, it might be explained in part by the dependence of many developing countries on volatile commodity export earnings. For companies with earnings driven by local conditions, higher volatility might also result from more pronounced business cycles in emerging markets, owing, for example, to greater liquidity constraints in these economies, which have less-developed financial markets. Finally, earnings may show greater volatility because of greater volatility in economic policies in emerging markets (see also Mullin (1993)).19
Another possibility is that the greater volatility of emerging market returns may be due to larger shifts in required rates of returns in emerging markets. The most important factor in this regard might be their lesser degree of integration into world markets because of various types of capital controls. The effects of the degree of integration on asset pricing can be explored in terms of the standard CAPM or mean-variance approach to asset pricing. According to the CAPM, the required return on an asset is determined by the expected covariance of its returns with the return on the relevant market portfolio, and by the risk premium on the market portfolio (which will depend on the variance of the return on that portfolio). In a segmented market, the relevant market portfolio for asset pricing will be the national market index. In these circumstances, an increase in country-specific risk cannot be diversified away, as would be possible in an integrated market, and the risk premium will increase and asset prices will fall. Closed markets should therefore be subject to greater variance in asset prices and returns than open markets (which have the benefits of diversification), and required rates of return (for example, as proxied by earnings-price ratios) will be higher in closed emerging markets. Required rates of return should fall and prices rise when markets are opened to foreign investors (see Bekaert (1995)).
IV. Testing for Predictability in Returns in Emerging Markets
The results of the previous section provide no support for the notion that the volatility of weekly or monthly returns in emerging markets has increased in recent years. Velasco (1993) notes, however, that the volatility of short-term returns may be of less concern to policymakers than the possibility of long swings in asset prices that are followed by large reversals. A related issue is the apprehension expressed by some authors about the possibility of speculative bubbles in emerging markets. If they occurred, bubbles would be characterized by above-normal, positively autocorrelated returns in the periods when the bubbles were building, followed by large negative returns when the bubbles burst. This type of volatility is addressed in the remainder of the paper by testing in three different ways for the predictability of long-horizon returns based on their own history.
Tests for Stationarity of, and Cointegration Between, Return Indices
The first methodology to be used to test for predictability in returns involves time-series tests for stationarity of return indices and for cointegration between return indices.20 The motivation for these tests is that each could potentially provide evidence of strong forms of return predictability. Other weaker forms of predictability are addressed in the following subsections. It should be noted that the tests in this subsection use total return indices, rather than simple returns, which are the first differences of return indices.
Tests for the order of integration of a return index reveal whether it is stationary or nonstationary.21 Stationarity or trend stationarity would indicate a strong form of predictability based on a series’ own recent history. In particular, a stationary return index consistently demonstrates reversion to its mean, while a trend-stationary index tends to return to its deterministic trend. For example, a period of high returns that took an index above its mean or trend would be followed by a period of low returns, during which the index would revert to its mean or trend.
Tests for cointegration can reveal an equally strong form of relative predictability. Cointegration between return indices would imply that the indices have a very strong tendency to move together in the long run. Cointegration would imply the rejection of a random walk model of stock prices and the existence instead of an error-correction mechanism (see Engle and Granger (1987)). In such a case, a period of overperformance in one market would be followed by an exactly offsetting period of underperformance, and vice versa. However, standard asset pricing models preclude cointegration between return indices in integrated markets, as asset prices or price indices should respond differently over time to shocks (Richards (1995)). Rejection of the null hypothesis of no cointegration would therefore imply that markets are inefficient, segmented, or both.22
The time-series tests use the end-quarter logarithm of the U.S. dollar excess return indices for all nine emerging markets for which data are available since the end of 1975. Tests for nonstationarity using the augmented Dickey-Fuller (ADF) test are shown in panel A of Table 1.23 The results indicate that the null hypothesis of nonstationarity is rejected only for India; all other indices are apparently nonstationary, and further tests (not shown) indicate that they are integrated of order one. This finding is consistent with standard asset pricing theory, which suggests that return indices should contain a random walk component (Richards (1995)) and should not demonstrate the extreme form of predictability implied by stationarity. The case of India may well be a Type I error (that is, a false rejection); this would not be surprising, given that nine tests are conducted, each with a 10 percent significance level.
Standard tests for cointegration are then conducted on those eight series that appear to be integrated of order one. Three forms of cointegration are investigated. First, I test for cointegration between the return index for each emerging market and the index for the MSCI (industrial country) world portfolio. That is, I test whether emerging market indices are linked by a long-run relationship to the world market index. The Engle and Granger (1987) methodology is used, with a time trend included in each case unless it appears statistically insignificant. The results, as shown in panel B of Table 1, provide strong evidence that emerging market indices are not coin-tegrated with the world index; the hypothesis of no cointegration is not rejected for any of the eight countries. These results confirm the expectation that cointegration—and a strong form of predictability—should not exist between return indices.
|A. Tests for Stationariiy of Excess Return Indices||B. Tests for Cointcgration with World Index|
|Market||ADF statistic||5 percent critical value||ADF statistic||5 percent critical value|
|MSCI world index||-2.17||-3.41||…||…|
|C. Tests for Bivariate Cointegration Between Markets|
|Significance level for ADF statistic|
|Number of country pairs||4||2||4||56|
|D. Tests for Cointegration Among Latin American Countries|
|Maximum eigenvalue test||Trace statistic test|
|Null hypothesis||Test statistic||5 percent critical value||Test statistic||5 percent critical value|
|r = 0||35.72**||30.16||57.12**||52.61|
Second, the Engle-Granger methodology is used to test for bivariate cointegrating relationships among all 56 possible pairs of emerging markets.24 The results, summarized in panel C of Table 1, indicate that the hypothesis of no cointegration is rejected at the 5(10) percent level in only 7(11) percent of cases. These rejections are little different from what is implied by the respective size of the tests, so there is very little evidence that emerging market indices are cointegrated with each other.25 This is further evidence of the need for caution in the burgeoning practice of regressing large numbers of price or return indices against each other in search of “cointegrating relationships.”
Finally, as it has sometimes been asserted that cointegrating relationships are likely in groups of countries within a similar region, I use the Johansen and Juselius (1990) methodology to test for a cointegrating system among the four Latin American countries (Argentina, Brazil, Chile, and Mexico) with data available since the end of 1975.26 The test results in panel D of Table 1 suggest the presence of a single cointegrating vector in the four-country system of Latin American markets: the hypothesis of zero cointegrating vectors is rejected, but the hypothesis of no more than one cointegrating vector is not rejected. An examination of the estimated vector suggests that the rejection is due to the apparent relationship between the return indices of Chile and Mexico. These countries were one of the few pairs for which the Engle-Granger tests in panel C suggested cointegration. This finding may again be an example of a Type I error: the relatively low short-run correlations between these countries suggest that it may be spurious.27 In any event, there is clearly no evidence for three cointegrating vectors. Therefore, despite the evidence that Latin American markets are sometimes subject to similar influences (see Calvo, Leiderman, and Reinhart (1993)), the return indices of the four Latin American countries are not all simply determined in the long run by a single common trend, which would imply a high degree of predictability in relative returns.
Regression Tests for Predictability
The regressions in this subsection comprise the second methodology to be used in testing for predictability in returns. These regressions test for a possibly weaker form of predictability than would be implied by the stationarity of a return index; in particular, they test for predictability of excess U.S. dollar returns based on their own history. Following Fama and French (1988), I test for long-horizon predictability in returns by regressing the k-month return upon the lagged k-month return, as follows:
where ri is the log difference in the relevant total return index for country i. 28 The expected value of the β(k) will depend on the time-series properties of the relevant total return index (Fama and French (1988)). If the index is a pure random walk with no stationary component, the estimated β(k) should, after correction for the usual negative bias in estimated autocorrelation coefficients, be equal to zero for all k. If the index contains a stationary component but no random walk component, the β(k) should be close to zero for low k and approach —0.5 for high k. Finally, if there is both a stationary and a random walk component, one might expect a U-shaped pattern for the β(k): close to zero for small k, moving toward —0.5 at those horizons where the transitory component is more important, and then returning to zero for large k, where the random walk component dominates.
The results of the regression tests are assessed using simulated critical values, for several reasons. First, it is necessary to adjust for the usual bias in estimated autocorrelations. Second, in order to maximize the power of the tests, given the short data set available, these tests use overlapping data. Simulated critical values are necessary to take account of the moving-average error term that is introduced by overlapping data, as standard asymptotic corrections (the Hansen-Hodrick and Newey-West corrections) have been shown to have poor properties in small samples in related tests (see Kim, Nelson, and Startz (1991)). Third, emerging market returns may be characterized by nonnormality (see, for example, Harvey (1995) and Claessens, Dasgupta, and Glen (1995a)), which renders many conventional test distributions incorrect.29 The simulated critical values used in this paper are therefore derived from the empirical distribution function rather than from standard Monte Carlo simulations, which assume normally distributed innovations.
Because the tests involve a null hypothesis of no predictability, simulations are used to generate “randomized” series that have no temporal dependence in returns. These series are constructed by cumulating the innovations drawn (without replacement) from the actual innovations in the logged return index. Tests for significance are based on the distribution of the β(k) under 1,000 randomizations under the null hypothesis of no predictability in returns.
The results of the regression tests for predictability in excess returns are shown in Table 2. The markets are divided into two groups: the first group (of nine markets) has data for December 1975-September 1995, and the second group (of seven markets) has data for December 1984-September 1995. The table shows bias-corrected regression estimates of β(k) (the actual estimate less its mean value from simulations), with rejections of the null of no predictability denoted by asterisks. While return horizons of 3, 6, 12, 24, and 36 months are shown for the first group of countries, the 36-month results are omitted for the second group because of the shorter data sample. Average 90 percent confidence intervals derived from simulations are shown for the two groups; exact confidence intervals vary by country.
The results indicate only a few rejections of the null hypothesis of no temporal dependence in returns. These results are primarily a function of the short data sample and wide confidence intervals: with fewer than 20 years of data, it is difficult to reject the null of no autocorrelation in returns, especially at longer horizons. Poterba and Summers (1988) argue that the use of higher-than-normal significance levels may be appropriate in these conditions. Indeed, even if a series were fully mean reverting at a three-year horizon (that is, β(36) = –0.5), the null of no temporal dependence in returns would only just be rejected, based on the average 5 percent test statistic (–0.45).
The few rejections that do occur, however, suggest a pattern of positive autocorrelation at short horizons and negative autocorrelations at longer horizons. All five rejections at the three- and six-month horizons indicate positive autocorrelations, while six of the nine rejections at longer horizons indicate negative autocorrelations. The strong rejections for India and the estimates for β of about —0.5 are not surprising, given the evidence of the previous subsection that the return index for India appears trend stationary. Also, the results for individual countries at the three-month horizon appear to be qualitatively consistent with the estimates of Claessens, Dasgupta, and Glen (1995a) for autocorrelations in one- and two-month returns. Results for real domestic currency returns (not shown) indicate a similar pattern.
|Market||3 months||6 months||12 months||24 months||36 months|
|December 1975-September 1995|
|Average 5 percent one-sided significance levels|
|HO: Beta < 0||-0.18||-0.22||-0.29||-0.40||-0.45|
|HO: Beta > 0||0.18||0.22||0.30||0.41||0.52|
|December 1984-September 1995|
|Taiwan Province of China||-0.10||-0.14||0.26||0.23||…|
|Average 5 percent one-sided significance levels|
|HO: Beta < 0||-0.24||-0.29||-0.39||-0.52||…|
|HO: Beta > 0||0.24||0.30||0.41||0.60||…|
Tests for Winner-Loser Effects Across Markets
Overall, the long-horizon results of the previous subsection do not yield firm evidence that returns are subject to the type of strong reversals that would be suggested by regular long swings or bubbles in asset prices. This finding may, of course, be a function of the problems of short samples and wide confidence intervals in regressions involving individual countries. A third methodology for testing for predictability in returns is suggested by the winner-loser approach introduced by DeBondt and Thaler (1985) in a study of stocks within the U.S. market. This methodology, which has been applied to developed country national markets by Richards (1995), provides a test for a weaker form of predictability of relative returns than would be implied by cointegration between return indices.
In this approach, at the end of each quarter, countries are placed into four hypothetical portfolios weighted by market capitalization, based on their relative performance in a ranking period. The performance of the portfolio is then simulated in the subsequent test period. The portfolio of countries that has performed worst (best) in the ranking period is referred to as the “loser” (“winner”) portfolio. The returns in the ranking and test periods quoted are annualized “market-adjusted returns,” that is, returns relative to the return on a portfolio of all emerging markets included in the test, weighted by market capitalizations. The average return measures quoted are calculated as the geometric average return of all return observations and are not subject to the problem of skewness in returns identified in Ball, Kothari, and Shanken (1995).30 Under the null hypothesis of no predictability in returns, there should be no correlation between average ranking-period portfolio returns and average test period portfolio returns.
The portfolio strategies replicated here should not be considered formal trading rules, as capital controls, thin markets, and restrictions on equity ownership would have rendered them impossible during most if not all of the sample period being tested. Instead, they should be thought of as a means of grouping countries so as to reduce country-specific noise and increase the low power of country-by-country tests, such as the regression tests presented above. Furthermore, while a notional “zero net investment” portfolio return is defined as the return on the loser portfolio minus the return on the winner portfolio, such a portfolio would not have been possible in reality because of the absence of short selling in most markets. Instead, this portfolio return should simply be thought as a means of summarizing the difference in returns on extreme winners and losers.
The tests use overlapping data, and the empirical significance of test period return differentials is, as with the regression tests for predictability, assessed by using simulated critical values based on randomizations. The innovations in the series are reshuffled in parallel to maintain within-period correlations in the return indices and capitalization weights while destroying any time dependence in returns. A null hypothesis of similar expected returns across all countries is assumed, and confidence intervals are based on the distribution in 1,000 randomizations of test period returns.31
Results for the group of eight countries with data available from December 1975 are shown in Table 3, with the countries divided into four portfolios of two countries each.32 Results for the group of 16 countries with returns data available since December 1984 are shown in Table 4, with the countries divided into four portfolios of 4 countries each. In each table, the upper two panels show the average annualized return on the portfolios in the ranking and test periods, respectively, while the bottom panel shows 90 percent confidence intervals for the ranking-period return. Returns are all relative to the market-weighted return on all countries. The final column in each table, which shows the results in real domestic currency terms, provides evidence that the results are not due solely to the use of U.S. dollar returns. The tests weight each portfolio by market capitalization, but similar results are obtained if equal-weighted portfolios are used.
The data in the top panel of Tables 3 and 4 indicate very large return differences in the ranking period, which are consistent with the high variance and low cross-correlations of short-term returns. For the three-month returns, for example, the winner-loser return differential is typically over 200 percent: the strongest markets typically outperform the average return by about 150 percent while the weakest markets underperform the average return by about 60 percent (with all returns quoted on an annualized basis). As expected, return differentials are reduced as the return horizon increases.
The test period returns in the middle panel of Tables 3 and 4 provide some evidence of predictability of returns. At short horizons, ranking-period winners appear to continue to outperform ranking-period losers, although confidence intervals are again wide because of the short sample and the high volatility of returns.33 The strongest positive autocorrelation appears at the six-month horizon in Table 3, where the annualized return differential of 11.8 percent is statistically significant at the 9 percent one-sided level. At horizons of one year or more, however, autocorrelations in returns appear to be negative. In statistical terms, the strongest predictability is at the three-year horizon in Table 3, where prior losers appear to outperform prior winners on average by 14.6 percent per year; this result is significant at the 6 percent one-sided level.
|U.S. dollar returns||Real returns|
|Number of months in period||Portfolio 1||Portfolio 2||Portfolio 3||Portfolio 4||Zero net investment||Zero net investment|
|Ranking-period average annual return|
|Test period average annual return|
|Empirical 90 percent confidence interval for test period return|
|3||-13.3, 13.8||-11.7, 13.1||-11.2, 11.3||-12.6. 13.8||-22.6. 20.9||-188.8.131.52|
|6||-11.0. 10.5||-9.0. 9.5||-9.0. 10.1||-9.7. 10.8||-18.8, 17.5||-17.9. 16.2|
|12||-10.0, 10.3||-7.6, 7.4||-7.0, 8.0||-9.5. 10.0||-17.5. 16.3||-14.8. 14.3|
|24||-9.4. 9.3||-6.8. 7.3||-6.7. 7.5||-8.8, 9.6||-15.4. 15.0||-15.6. 14.5|
|36||-9.5, 9.0||-6.8, 7.6||-6.7, 8.7||-9.1, 9.6||-14.7, 15.1||- 14.4. 13.6|
|U.S. dollar returns||Real returns|
|Number of months in period||Portfolio 1||Portfolio 2||Portfolio 3||Portfolio 4||Zero net investment||Zero net investment|
|Ranking-period average annual return|
|Test period average annual return|
|Empirical 90 percent confidence interval for test period return|
|3||-13.7, 15.8||-13.4. 13.9||-12.8, 13.4||-13.0, 13.9||-22.4. 24.8||-24.2, 24.1|
|6||-12.4, 12.8||-9.5, 10.1||-9.3. 9.3||-10.8, 12.1||-21.5, 20.5||-21.3, 20.6|
|12||-10.6, 10.2||-7.6, 8.2||-7.2, 7.9||-10.3, 10.2||-17.9, 17.2||-17.9, 17.0|
|24||- 10.1, 10.6||-7.0, 7.6||-6.8. 7.7||-9.2, 8.9||-16.0, 16.8||- 16.8, 16.0|
The returns on the zero net investment portfolio for emerging markets for the three-year return horizon are shown in Figure 7. Returns on this portfolio are highly variable, even if they have tended to be positive. Some preliminary attempts to assess whether return differentials are related to risk have not, however, been successful.34
Figure 7.Difference in Returns on Winner and Loser Portfolios in Eight Emerging Markets
Note: Three-year test period return on loser portfolio less return on winner portfolio, both calculated relative to average return in eight emerging markets (Argentina, Brazil, Chile, Greece, India, Korea, Mexico, and Thailand).
The return reversals in emerging markets are compared with those in the 16 mature markets in Table 5. To compare the strength of any mean reversion in the return on the zero net investment portfolio, the ratio of the average test period return to the average ranking-period return is shown in the bottom panel. This ratio, which is calculated using log-differenced data, is referred to as the “implied autocorrelation coefficient” on the zero net investment portfolio. Confidence intervals for test period outcomes are not shown but are fairly large, given the short sample period and the small number of countries in the portfolios. The conclusions that follow should therefore be considered indicative rather than statistically significant.
The results in the top panel indicate that ranking-period return differentials are typically far larger in emerging markets than in mature markets. Furthermore, return differentials appear to be larger in the smaller industrial country markets than in the larger markets. These results are consistent with the evidence presented in Section III on short-term volatility in these country groups. The results in the lower two panels suggest positive autocorrelation in returns at the three- and six-month horizons in all markets, with mature markets apparently demonstrating positive autocorrelations as large as those in the emerging markets. The data also suggest that return reversals occur at longer horizons in all three groups of markets. The reversals in emerging markets seem, however, to be larger and to appear more quickly: at the one-year horizon, emerging market returns have already demonstrated reversals while returns in mature markets still demonstrate positive autocorrelations. If the return reversals were evidence of fads or bubbles, one might conclude that liquidity in emerging markets is lower and therefore less able to maintain stock prices far from fundamentals over an extended period of time.
|Dec. 1975–Sept. 1995||Dec. 1984–Sept. 1995|
|Number of months in period||8 large, mature markets||8 small, mature markets||8 emerging markets||16 mature markets||16 emerging markets|
|Average annual ranking-period return differentials (percent)|
|Average annual test period return differentials (percent)|
|Average reversal coefficient|
The possible importance of fads, bubbles, or other mean-reverting behavior can perhaps be best addressed by considering the magnitude of the autocorrelation coefficients in the bottom panel of Table 5. As noted in the previous subsection, a fully mean-reverting series would be characterized by an autocorrelation coefficient of –0.5 at the horizon where the mean reversion is complete.35 The data suggest that, although none of the groups of countries demonstrate this degree of mean reversion, the smaller mature markets appear to be closest. The large confidence intervals around such estimates should again be stressed. Nonetheless, the data would suggest that, while larger in absolute terms, the reversals in emerging markets may well be smaller in relative terms than in some mature markets. Concern over the possibility of return reversals in emerging markets may therefore be somewhat overdone, at least compared with the possibility of similar behavior in the smaller mature equity markets.
The results of this paper cast doubt on two widely accepted “facts” about emerging equity markets. First, there appears to be little evidence to support the assertion that the volatility of returns in emerging markets has increased in recent years. Second, while there is evidence of positive autocorrelation in emerging market returns at horizons of up to about six months, there is only mixed evidence for subsequent negative autocorrelation as would be implied by models of investor overreaction or bubbles in stock prices.
The analysis of this paper suggests that emerging equity market returns, although always volatile, may actually have become less volatile in the recent period of increased foreign participation. In the preliberalization period, domestic capital may not have been “hot” in the sense that it could easily move abroad, but it appears nonetheless to have been able to bring about large changes in asset prices. The opening of markets allows more investors to share a given amount of risk and should therefore reduce the volatility of returns. The misperception that volatility has increased in recent years may have arisen simply because investors in industrial countries now have larger holdings of emerging market equities and therefore pay greater attention, along with the international financial press, to large price changes in these markets.
This finding on volatility also cautions against attributing excessive importance to foreign investors in the determination of asset prices. As indicated by the Mexican crisis of 1994–95, in which domestic investors appear to have been the major players in capital outflows (see Folkerts-Landau and Ito (1995)), domestic investors may still play the dominant role in determining the volatility and level of asset prices. More generally, based on an analysis of balance of payments data, Claessens, Dooley, and Warner (1995) caution that supposedly hot short-term capital flows may in fact show no more volatility than supposedly stable long-term flows.
The second finding of the paper relates to the predictability of returns. Returns at short horizons, notably of about one or two quarters, appear to be positively autocorrelated. However, at longer horizons, there is evidence from winner-loser tests of negative autocorrelation in relative returns. The estimated return differentials on winner and loser portfolios—at 10 percent a year or more—are economically significant but show only marginal statistical significance because of the relatively short data sample that is available.
Positive autocorrelation in short-horizon returns might conceivably be explained by insider trading by market participants who have superior information to others (Claessens, Dasgupta, and Glen (1995a)). Alternatively, it might be due to a group of uninformed traders, perhaps including foreign institutional investors (International Monetary Fund (1995)), who simply extrapolate past return behavior. The actions of such uninformed traders might also help to explain negative autocorrelation at longer horizons because these traders might cause prices to overreact to information flows, requiring subsequent correction. However, there is evidence for the same pattern of autocorrelations in markets in industrial countries, which implies that mature markets might be subject to the same “imperfections” as emerging markets.36
While positive autocorrelations and subsequent return reversals would be consistent with various models of investor overreaction or fads, it should not be forgotten that they may also be compatible with equilibrium models of time-varying risk and required returns. Risk, however, is difficult to assess in emerging markets owing to structural change, short-sample problems, and segmentation from the world market. In the end, therefore, one can do little more than speculate on the causes of the observed patterns in return autocorrelations.
The return reversals estimated in this paper are only moderately larger in absolute terms than those observed in industrial countries. Moreover, relative to their ranking-period return differentials, the return reversals in emerging markets may actually be smaller than those in some mature markets. One interpretation of these results is that in the period studied here fads or bubbles have not been especially frequent in emerging markets, or at least have been no more significant than in some mature markets.
The results of this paper appear to bolster the case for liberalizing and opening financial markets in developing countries. At the macroeconomic level, opening financial markets allows residents and foreigners to engage in mutually beneficial consumption smoothing and risk diversification. As risk is spread more widely, smaller changes in asset prices will be required to equalize the demand and supply of risky assets. The required rate of return on domestic projects (that is, the cost of capital) should therefore fall in response to market liberalization.
At the microeconomic level, the reductions in controls on capital flows into national equity markets may bring a host of benefits, including greater efficiency of asset pricing and a demand for higher standards of financial reporting and for legal frameworks that are more favorable to investment. Furthermore, the growth of stock markets may increase the volume of long-term investment, as the liquidity of stock markets allows risk-averse savers to participate in the financing of long-term investment projects. For example, Levine and Zervos (1995b) provide evidence that countries with more liquid stock markets in 1976 experienced above-average growth in both investment and GDP in the period 1976–93.
Of course, to the extent that capital inflows are excessive, the liberalization of markets may lead to unwarranted appreciation of the real exchange rate, unsustainable current account deficits, and bubbles in asset prices (see Folkerts-Landau and Ito (1995)). Investment in emerging markets may at times be driven less by the economic outlook in the emerging markets and more by factors such as the low level of returns in mature markets (Calvo, Leiderman, and Reinhart (1993)). It is also possible that institutional investors may view all emerging markets as a single asset class, so that shocks in one country may be transmitted to other markets with little economic rationale (International Monetary Fund (1995)).
While these factors may sometimes cause large movements in market prices, the volatility estimates presented in this paper indicate that similar or larger price movements occurred also in the preliberalization period. However, the nature of these price changes may recently have changed. In particular, if equity markets were closed to foreigners, the major impact of a change in expectations would be on domestic currency asset prices, perhaps with little effect on the economy as a whole. However, when foreign investors participate and foreign exchange is fully convertible, the impact falls more on the foreign exchange market. The effects of such shocks to the exchange rate may well be far larger and felt by more sectors of the economy than the effects of equivalent changes in stock prices in small, undeveloped equity markets.
To minimize the likelihood of damaging swings in asset prices and capital flows, national authorities should focus primarily on avoiding unsustainable economic policies. Strong prudential regulation and supervision may help to avoid such swings and—should they occur—minimize the subsequent damage. There may also be a case for gradual liberalization and a focus on structural reform to ensure that capital does not flow in too rapidly for an economy to absorb. Finally, the publication of economic statistics on a regular and timely basis may also help: economic news that is released in stages may be less destabilizing than news released later in a single announcement, in part because the initial release of information may prompt an earlier policy response to an imbalance. It seems unlikely, however, that volatility and swings in asset prices will be caused solely by the growth and liberalization of markets or by the participation of foreigners. Indeed, provided appropriate economic policies are in place, return volatility in emerging equity markets should gradually fall toward industrial country levels.
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Anthony J. Richards is an Economist in the European II Department. He is a graduate of Harvard University. The author would like to thank Julian Berengaut, Mohamed El-Erian, Ross Levine, Henri Lorie, Carmen Reinhart, Heidi Willmann Richards, and Thomas Walter for comments, and Linda Galantin for preparing the charts.
The term “country” or “market,” as used in this paper, does not in all cases refer to a territorial entity that is a state as understood by international law and practice; the term also covers some territorial entities that are not states, but for which statistical data are maintained and provided internationally on a separate and independent basis.
See also Gooptu (1993) for a discussion of recent trends and market participants and institutions, as well as El-Erian and Kumar (1995) and Feldman and Kumar (1995) for a discussion of the benefits of the liberalization of equity markets.
I am grateful to the International Finance Corporation and Morgan Stanley Capital International for providing some of the data used in this study.
Nontrading biases are likely to cause positive autocorrelation in short-term returns in emerging markets and an underestimation of the short-term return variances used in variance-ratio tests. Also, asynchronous trading may suggest that returns in the United States predict returns in Asian countries. Finally, as market activity increases and nontrading effects are reduced, price indices are less “time-averaged,” so that short-term returns calculated from market indices appear to show greater volatility. All these effects are spurious.
Richards (1996) shows, for example, that winner-loser reversals identified in industrial countries are primarily an equity market effect.
Exchange regime changes in Nigeria in January 1994 and March 1995 illustrate a potential problem with the use of U.S. dollar series. The EMDB switched in January 1994 from a parallel rate to the official rate and then in March 1995 to a newly introduced free market rate, resulting in an apparent exchange rate gain of about 100 percent, followed by an apparent loss of about 75 percent. Because of these problems, Nigeria is omitted from the analysis. There are apparently no other examples of such extreme regime changes in the IFC data (International Finance Corporation (1995)).
Consumer price data are from the IFS, with the exception of Taiwan Province of China’s data, which come from its Monthly Bulletin of Statistics.
The equation for expected returns is clearly simplistic: no attempt is made to model price jumps at regime changes, such as liberalizations, and the measure of volatility may therefore be overstated at such times.
The frequency of large price changes is also used by Folkerts-Landau and Ito (1995) and El-Erian and Kumar (1995) as a measure of volatility; those studies, however, concentrate only on falls in prices.
Fixed weights are used so that average volatility estimates are not affected by shifts in the relative market capitalization of high- and low-volatility countries: the results do not, however, appear especially sensitive to the use of fixed weights.
For example, using the P1 index of stock market development of Demirgüç-Kunt and Levine (1995), which is normally distributed around zero, the average development indices for the larger mature, smaller mature, and emerging markets are 1.07, 0.10, and –0.30, respectively.
In contrast to these measures, which appear to show some correlation in volatility, Kim and Singal (1993) find no correlation between emerging and developed market volatility. The positive correlations in this study might be due to the use of rolling-average measures of volatility rather than noisy monthly measures, and of U.S. dollar returns rather than domestic currency returns.
The statistical significance of the decline was assessed by regressing the volatility measure on 12 seasonal dummies and a dummy for the later period. Standard errors were obtained by using the Newey-West correction with 12 lags, to account for the method of generating the dependent variable.
The statistical significance of the changes was estimated by regressing the volatility measure on a constant and a dummy for the later observations. Standard errors were obtained by using the Newey-West correction with 13 lags, to account for the method of generating the dependent variable.
Markets were liberalized significantly around the middle of the sample in one of the countries (Colombia) that experienced an increase in return volatility. However, there were also liberalizations in some of the markets that experienced falls in volatility (for example, Argentina and Brazil) or no change in volatility (Korea), so no relationship between liberalization and volatility can be inferred. See, for example, Kim and Singal (1993) for a listing of significant liberalizations in EMDB markets.
One possibility is that, although the underlying volatility of returns in emerging markets has increased, “peso problems” have not yet revealed the full impact of this shift. Such hypotheses are impossible to rebut or confirm except with more data.
Levine and Zervos (1995a) present some conflicting evidence that volatility may increase after liberalization, but those authors also show that liberalization leads to increased integration into world markets. Any increase in volatility may be fairly benevolent, as those authors show in another work (Levine and Zervos (1995b)) that volatility does not affect economic growth, and that increased integration is associated with higher growth.
Alternatively, the higher correlations might be attributed to increasing (irrational) “contagion” effects between emerging markets. If so, this contagion is still fairly benign because it has not prevented declines in the volatility of individual emerging markets.
See Hausmann and Gavin (1995) for a discussion of the causes of business cycle volatility in Latin America and Bekaert and Harvey (1995b) for cross-sectional tests of factors associated with volatility.
To be precise, the null hypothesis for these tests makes them tests of non-stationarity and non-cointegration.
There is no connection between the time-series concept of integration and the capital markets concept of integration of financial markets; the latter concept requires that assets of similar risk have similar required rates of return even if traded in different markets (see Richards (1995)).
Granger (1986) also asserts that cointegration will be contrary to market efficiency, while Loughran and Newbold (forthcoming) conclude from an empirical study that “it is perhaps overly cynical to suggest that applying cointegration tests to financial markets data is a superb mechanism for generating random numbers. Nevertheless, the inconsistencies in results reported in applied studies … suggest extreme caution in inferring structural conclusions from the test statistics.”
For all ADF tests, the number of augmentation terms was selected by using the “Akaike plus 2” rule in Version 4.3 of TSP, with a maximum of eight lags. The tests for nonstationarity allow for a deterministic trend.
The tests are conducted “in both directions,” that is, with each country as both dependent and independent variable, resulting in 56 country pairs.
The apparent evidence that rejections of the null hypothesis are slightly more frequent than the size of the test may be related to a transitory or mean-reverting component in returns (see the subsection on “Tests for Winner-Loser Effects Across Markets”), which may occasionally dominate the permanent component in small samples and cause return indices to appear to be cointegrated.
A constant term is included in both the vector autoregression (VAR) and differenced equations. Critical values are from Table AI of Johansen and Juselius (1990), multiplied by T/(T — nj) (that is, 1.11) to offset the poor small-sample properties of these tests, where T = 78 is the number of observations in the VAR equation, j = 2 is the number of lags in the VAR, and n = 4 is the number of variables.
Also, trade links between Mexico and Chile are quite small: trade between them in 1994 accounted for about 2 percent of Chile’s total trade and about 0.4 percent of Mexico’s.
Log differences are used to avoid potential econometric problems arising from the skewness in conventional percentage returns, which are bounded below at minus 100 percent. Such problems may be more extreme than usual because of the long horizons and the high volatility of emerging market returns.
The strong findings of nonnormality in previous work may, however, be due in part to the use of percentage returns that are bounded below at minus 100 percent and therefore cannot strictly be normally distributed. To examine this, I tested the quarterly series on excess returns for normality using the Jarque-Bera test. At the 5 percent significance level, the hypothesis of normality was rejected for 13 of 16 countries with conventional percentage return data; however, the hypothesis was rejected for only 7 out of 16 countries with log-differenced returns. The median reduction in the test statistic from the use of log-differenced returns was nearly 80 percent.
See Ball, Kothari, and Shanken (1995) for further discussion of possible methodological problems in winner-loser tests.
Monte Carlo simulations show that, when innovations for all countries have a similar mean, winner-losers tests have a zero expected return, and there is no bias corresponding to the autocorrelation bias in regression tests.
Zimbabwe, which has the smallest market capitalization of the nine countries with data from the end of 1975, is omitted from the tests in Table 3.
The three-month results would appear to suggest counterintuitively that all four ranked portfolios may on average outperform the market portfolio in the test period. This result is due to the combination of (1) a negative correlation between portfolio returns and the share in total market capitalization of the portfolios, a form of “small country effect”; and (2) the use of an unweighted (geometric) average of all outcomes in the calculation of the average portfolio returns over time, regardless of the relative size of the portfolios.
In a preliminary attempt to investigate whether risk factors might explain the test period differences in returns, I estimated both the average variance of returns and the average world market beta for the four ranked portfolios, concentrating on the two-year return horizon. The return variance was proxied by the variance of the eight test-period quarterly returns, while average world market betas were estimated based on two-year total test period returns. Two-year returns were chosen because they should increase the probability of obtaining substantial differences in betas.
The results of this initial exploration were not promising. While two-year returns on the emerging market portfolios were correlated with the world return and the beta on the winner portfolio was estimated to be lower than the beta on the loser portfolio, the difference was neither statistically significant nor large enough (based on standard estimates of the equity risk premium) to explain more than a small part of the return differential. When betas were allowed to be linear functions of time, no consistent pattern for increases over time emerged, as would be suggested by increasing integration into the world market. Nor did the variance of returns play the role that might have been expected if markets were segmented: the average variance of returns on the loser portfolio was actually somewhat below that of the winner portfolio. Furthermore, regressions indicated that above-average returns on the portfolios were not generally associated with above-average variance outcomes. Finally, regression models of portfolio returns that allowed for time-varying impacts from the world market and the own-variance were not promising.
Three reasons can be advanced for the failure of this exploratory test. First, it may reflect the general failure of asset pricing models in emerging markets. Second, the failure may b e due to the very simple measures of risk used and the assumption that the risk of the winner and loser portfolios, despite the continual changes in their composition, is a fairly stable concept. Finally, the failure may indicate the need for a more sophisticated treatment of time variation in risk, possibly along the lines of Bekaert and Harvey (1995a).
For this test, however, it is possible that an autocorrelation smaller in absolute magnitude than –0.5 could be consistent with full mean reversion. If different markets demonstrated full mean reversion at different horizons, an average auto-correlation coefficient, such as is derived here, might never reach –0.5.
For example, Chan, Jegadeesh, and Lakonishok (1995) provide evidence that the returns on even large U.S. companies display significant positive autocorrelation, or “momentum,” over periods of several quarters.