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.

Up-to-Date Research Sheds New Light on This Area Taking into account the ongoing worldwide financial crisis, Stock Market Volatility provides insight to better understand volatility in various stock markets. This timely volume is one of the first to draw on a range of international authorities who offer their expertise on market volatility in devel

Despite concerns are often voiced on the so called “excess volatility” of the stock market, little is known about the implications of market volatility for the real economy. This paper examines whether the stock market volatility affects real fixed investment. The empirical evidence obtained from the US data shows that market volatility has independent effects on investment over and above that of stock returns. Volatility and its changes are negatively related to investment growth. To the extent volatility depresses fixed capital formation and hence future income growth, the results suggest the desirability of reducing stock market volatility.

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 paper presents a comprehensive study of the interactions among returns, volatility, and trading volume between the U.S. and Japanese stock markets by using intradaily data from October 1985 to December 1991. By examining the effect of foreign price volatility and trading volume on correlations between foreign and domestic stock returns, the paper aims to distinguish between the market contagion and informational efficiency hypotheses in order to explain the cause of international transmission of stock returns and volatility. Major findings are three-fold: (1) contemporaneous correlations of stock returns across these two markets are significant and tend to increase during a high volatility period, which support the informational efficiency hypothesis; (2) lagged volatility and volume spillovers are not found across the two markets; (3) the effect of the New York stock returns on the Tokyo returns exhibits a structural change in October 1987.

In this paper we study the dynamic relationship between trading volume, volatility, and stock returns at the international stock markets. First, we examine the role of volume and volatility in the individual stock market dynamics using a sample of ten major developed stock markets. Next, we extend our analysis to a multiple market framework, based on a large sample of cross-listed firms. Our analysis is based on both semi-nonparametric (Flexible Fourier Form) and parametric techniques. Our major findings are as follows. First, we find no evidence of the trading volume affecting the serial correlation of stock market returns, as predicted by Campbell et.al (1993) and Wang (1994). Second, the stock market volatility has a negative and statistically significant impact on the serial correlation of the stock market returns, consistent with the positive feedback trading model of Sentana and Wadhwani (1992). Third, the lagged trading volume is positively related to the stock market volatility, supporting the information flow theory. Fourth, we find the trading volume to have both an economically and statistically significant impact on the price discovery process and the co-movement between the international stock markets. Overall, these findings suggest the importance of the trading volume as an information variable.