Understanding volatility in emerging capital markets is important for determining the cost of capital and for evaluating direct investment and asset allocation decisions. We provide an approach that allows the relative importance of world and local information to change through time in both the expected returns and conditional variance processes. Our time-series and cross-sectional models analyze the reasons that volatility is different across emerging markets, particularly with respect to the timing of capital market reforms. We find that capital market liberalizations often increase the correlation between local market returns and the world market but do not drive up local market volatility.

Heteroscedasticity biases tests for contagion based on correlation coefficients. When contagion is defined as a significant increase in market co-movement after a shock to one country, previous work suggests contagion occurred during recent crises. This paper shows that correlation coefficients are conditional on market volatility. Under certain assumptions, it is possible to adjust for this bias. Using this adjustment, there was virtually no increase in unconditional correlation coefficients (i.e., no contagion) during the 1997 Asian crisis, 1994 Mexican devaluation, and 1987 U.S. market crash. There is a high level of market co-movement in all periods, however, which we call interdependence.

Testing the hypothesis that international equity market correlation increases in volatile times is a difficult exercise and misleading results have often been reported in the past because of a spurious relationship between correlation and volatility. Using "extreme value theory" to model the multivariate distribution tails, we derive the distribution of extreme correlation for a wide class of return distributions. Empirically, we reject the null hypothesis of multivariate normality for the negative tail, but not for the positive tail. We also find that correlation is not related to market volatility per se but to the market trend. Correlation increases in bear markets, but not in bull markets.

University. We are especially grateful for suggestions from Geert Bekaert, Bob Hodrick, and Ken Singleton. We also thank an anonymous referee whose comments and suggestions greatly improved the paper.

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This paper surveys the most important developments in multivariate ARCH-type modelling. It reviews the model specifications and inference methods, and identifies likely directions of future research.

Contagion in equity markets refers to the notion that markets move more closely together during periods of crisis. One of the most interesting aspects of the contagion debate is the disagreement

This paper inv estigates the potential for extreme co-mov ements between financial assets by directly testing the underlying dependence structure. In particular, a t-dependence structure, deriv ed from the Student t distribution, is used as a proxy to test for this extremal behav#a(0 Tests in three di#erent markets (equities, currencies, and commodities) indicate that extreme co-mov ements are statistically significant. Moreov er, the "correlation-based" Gaussian dependence structure, underlying the multiv ariate Normal distribution, is rejected with negligible error probability when tested against the t-dependencealternativ e. The economic significance of these results is illustratedv ia three examples: co-mov ements across the G5 equity markets; portfoliov alue-at-risk calculations; and, pricing creditderiv ativ es. JEL Classification: C12, C15, C52, G11. Keywords: asset returns, extreme co-mov ements, copulas, dependence modeling, hypothesis testing, pseudo-likelihood, portfolio models, risk management. # The authorsw ould like to thankAndrew Ang, Mark Broadie, Loran Chollete, and Paul Glasserman for their helpful comments on an earlier version of this manuscript. Both authors arewS; the Columbia Graduate School of Business, e-mail: {rm586,assaf.zeevi}@columbia.edu, current version available at www.columbia.edu\# rm586 1 Introducti7 Specification and identification of dependencies between financial assets is a key ingredient in almost all financial applications: portfolio management, risk assessment, pricing, and hedging, to name but a few. The seminal work of Markowitz (1959) and the early introduction of the Gaussian modeling paradigm, in particular dynamic Brownian-based models, hav e both contributed greatly to making the concept of co rrelatio almost synony...

We review the international finance literature to assess the extent to which international factors affect financial asset demands and prices. International asset pricing models with mean-variance investors predict that an asset’s risk premium depends on its covariance with the world market portfolio and, possibly, with exchange rate changes. The existing empirical evidence shows that a country’s risk premium depends on its covariance with the world market portfolio and that there is some evidence that exchange rate risk affects expected returns. However, the theoretical asset pricing literature relying on mean-variance optimizing investors fails in explaining the portfolio holdings of investors, equity flows, and the time-varying properties of correlations across countries. The home bias has the effect of increasing local influences on asset prices, while equity flows and cross-country correlations increase

We examine international stock return comovements using country-industry and country-style portfolios as the base portfolios. We first establish that parsimonious risk-based factor models capture the covariance structure of the data better than the popular Heston-Rouwenhorst (1994) model. We then establish the following stylized facts regarding stock return comovements. First, we do not find evidence for an upward trend in return correlations, except for the European stock markets. Second, the increasing importance of industry factors relative to country factors was a short-lived, temporary phenomenon. Third, we find that large growth stocks are more correlated across countries than are small value stocks, and that the difference has increased over time. JEL Classification: C52, G11, G12.