Conditional Heteroscedasticity and Stock Market Returns: Empirical Evidence from Morocco and BVRM

Abstract

Using data from Casablanca (Morocco) and Bourse Régionale des Valeurs Mobilières (BVRM) stock markets, this paper investigates and compares different distribution density and forecast methodology of three generalised autoregressive conditional heteroscedasticity (GARCH) models for Morocco and BVRM indices. The symmetric GARCH and asymmetric Glosten Jagannathan and Runkle (GJR) version of GARCH (GJR-GARCH) and Exponential GARCH methodology are employed to investigate the effect of stock return volatility in both stock markets using Gaussian, Student-t and Generalised Error distribution densities. The study further examines the forecasting ability of each GARCH model using alternative densities. In both markets, the EGARCH results show that negative shocks will have a greater impact on future volatility than positive shocks of the same magnitude, confirming the existence of leverage effect. However, for both markets, the GJR estimates imply that positive instead of negative shocks will have a higher next period conditional variance. This means that positive instead of negative shocks would have greater effects on next period volatility. Regarding forecasting evaluation, the results reveal that the symmetric GARCH model coupled with fatter-tail distributions present a better out-ofsample forecast for both stock markets.