Abstract
► GARCH-class models with Student-t distributions are used to examine the volatility relationships between stock returns and exchange rates. ► Strong evidence of asymmetric reaction to news and long memory in the conditional volatility processes is found. ► Univariate FIAPARCH and bivariate CCC-FIAPARCH models provide more accurate volatility estimates and forecasts than the other competing specifications. ► FIAPARCH model is particularly useful in measuring and forecasting market risk exposure for diversified stock and currency portfolios.
We use univariate and multivariate GARCH-type models to investigate the properties of conditional volatilities of stock returns and exchange rates, as well as their empirical relationships. Taking three European stock markets and two popular US dollar exchange rates as case study, our results show strong evidence of asymmetry and long memory in the conditional variances of all the series considered. In multivariate settings we find that bilateral relationships between stock and foreign exchange markets are highly significant for France and Germany. Moreover, both the univariate FIAPARCH and bivariate CCC-FIAPARCH models provide more accurate in-sample estimates and out-of-sample forecasts than the other competing GARCH-based specifications in almost all cases. Finally, there is evidence to support the suitability of the FIAPARCH model in forecasting portfolio's market risk exposure and the existence of diversification benefits between stock and foreign exchange markets.