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.

Empirical Studies on Volatility in International Stock Markets describes the existing techniques for the measurement and estimation of volatility in international stock markets with emphasis on the SV model and its empirical application. Eugenie Hol develops various extensions of the SV model, which allow for additional variables in both the mean and the variance equation. In addition, the forecasting performance of SV models is compared not only to that of the well-established GARCH model but also to implied volatility and so-called realised volatility models which are based on intraday volatility measures. The intended readers are financial professionals who seek to obtain more accurate volatility forecasts and wish to gain insight about state-of-the-art volatility modelling techniques and their empirical value, and academic researchers and students who are interested in financial market volatility and want to obtain an updated overview of the various methods available in this area.

This study examines the relationship between expected stock returns and volatility in the twelve largest international stock markets during January 1980 - December 2001. Consistent with most previous studies, we find a positive but insignificant relationship during the sample period for the majority of the markets based on parametric EGARCH-M models. However, using a flexible semiparametric specification of conditional variance, we find evidence of a significant negative relationship between expected returns and volatility in six out of the twelve markets under study. The results lend support to the recent claim (Bekaert and Wu, 2000; Whitelaw, 2000) that stock market returns are negatively correlated with stock market volatility.

Modern Finance and Risk Management is dedicated to our colleague, academic mentor, and adviser Professor Hermann Locarek-Junge. During his academic career, Hermann Locarek-Junge published several important contributions to the field of risk management and portfolio management and served as the chairman and board member of the German Finance Association (DGF) and the Data Science Society (Gesellschaft für Klassifikation).A short foreword by the mentors of Hermann Locarek-Junge and an introduction by the editors mark the beginning of the Festschrift. The first section on Modern Finance includes chapters on asset management, entrepreneurship, and behavioural finance. The second section on Modern Risk Management contains seven contributions covering considerations of risk measurement, risk management, and regulation. Finally, the third section includes topics on commodities and energy finance.This Festschrift comprises 20 original contributions of notable scholars in finance who have worked with Hermann Locarek-Junge over the last four decades. Due to numerous connections to practice and applications, Modern Finance and Risk Management is relevant and attractive not only to academics and researchers but also to practitioners in industry and banking.