Benefits to portfolio diversification depend crucially on correct correlation estimates, hence it is of great importance to both risk management and portfolio optimisation that the exact nature of the correlation structure between international financial assets is understood. Recent discussion on the correlation of international equity returns has focussed on the issue of whether extreme movements in international financial markets are more highly correlated than usual returns. This implies a reduction in the benefits from portfolio diversification since extreme returns are more likely to occur with greater simultaneity. Using the Value-at-Risk methodology we are able to measure the quantile correlation structure implicit in international asset returns in a simple manner without having to resort to fully parametric modelling. We illustrate that the extraction of the quantile covariance structure from this quantile correlation structure is non-trivial. Using daily data on stock market indices for a variety of countries we observe how the correlation and covariance structure changes as we move into the tails of the return distribution. We find for extreme stock market movements the benefits to international diversification are significantly curtailed even after discarding spurious correlation changes.

We examine the co-movements of equity returns in four major international markets by characterizing the time-varying cross-country covariances and correlations. Using a generalized positive definite multivariate GARCH model, we find that the Japanese and U.S. stock markets have significant transitory covariance, but zero permanent covariance. The other pairs of markets examined display significant permanent and transitory covariance. We also find that, while conditional correlations between returns are generally small, they change considerably over time. An event analysis suggests that basing diversification strategies on these conditional correlations is potentially beneficial.

How the correlation between equity returns behaves during market turmoils has been an issue of discussion in the international finance literature. Some research suggest an increase of correlation during volatile periods [Ang and Bekaert, 2002], while others argue its stability [Forbes and Rigobon, 2002]. In this paper, we study the impact of returns and volatility on correlation between international equity markets. Our objective is to determine if there is any asymmetry in correlation and identify the main explanation for this asymmetry. Within a framework of autoregressive models we quantify the relationship between return, volatility, and correlation using the generalized impulse response function and we test for the asymmetries in the return-correlation and volatility-correlation relationships. We also examine the implications of these asymmetric effects for the optimal international portfolio. Empirical evidence using weekly data on US, Canada, UK, and France equity indices, show that without taking into account the effect of return, there is an asymmetric impact of volatility on correlation. The volatility seems to have more impact on correlation during market upturn periods than during downturn periods. However, once we introduce the effect of return, the asymmetric impact of volatility on correlation disappears. These observations suggest that, the relation between volatility and correlation is an association rather than a causality. The strong increase in the correlation is driven by the market direction and the level of return rather than the level of the volatility. These results are confirmed using some tests of the asymmetry in volatility-correlation and return-correlation relationships in separate models and then in a joint model. Finally, we find that taking into account the asymmetric effect of return on correlation leads to an average financial gain ranged between 3.35 and 37.25 basis points for optimal international diversification.

We examine international stock return comovements using country-industry and country-style 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, excpet for the European stock markets. Second, the increasing imporatnce of industry factors relative to country factors was a short-lived, temporary phenomenon. Third, we find no evidence for a trend in idiosyncratic risk in any of the countries we examine.

This timely volume addresses three important recent trends in the internationalization of United States equity markets: extensive market integration through foreign investment and links among stock prices around the world; increasing securitization as countries such as Japan come to rely more than ever before on markets in equities and bonds at the expense of banks; and the opening of national financial systems of newly industrializing countries to international financial flows and institutions, as governments remove capital controls and other barriers. Eight essays examine such issues as the current extent of international market integration, gains to U.S. investors through international diversification, home-country bias in investing, the role of time and location around the world in stock trading, and the behavior of country funds. Other, long-standing questions about equity markets are also addressed, including market efficiency and the accuracy of models of expected returns, with a particular focus on variances, covariances, and the price of risk according to the Capital Asset Pricing Model.

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. This paper focuses on extreme correlation, that is to say the correlation between returns in either the negative or positive tail of the multivariate distribution. Using ldquo;extreme value theoryrdquo; to model the multivariate distribution tails, we derive the distribution of extreme correlation for a wide class of return distributions. Using monthly data on the five largest stock markets from 1958 to 1996, 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.