Abstract:
Using empirical evidence from East and North Africa Stock Markets, this paper examines and compares
alternative distribution density forecast methods of three generalised autoregressive conditional
heteroscedasticity (GARCH) models. We employed the symmetric GARCH, Glosten Jagannathan and
Runkle version of GARCH (GJR-GARCH) and Exponential GARCH methods to investigate the effect of
stock return volatility using Gaussian, Student-t and Generalised Error distribution densities. The results
show that the use of GJR and EGARCH with non-normal distribution densities appear justified to model
the asymmetric characteristics of both indices. The evidence so far shows that in both markets, negative
shocks would generally have a greater impact on future volatility than positive shocks, confirming the
existence of leverage effect. The presence of leverage effect suggests that investors in these markets
should be rewarded for taking up additional leverage risk as a fall in equity value (resulting from
volatility) would mean a rise of debt to equity ratio and therefore, increase in financial distress risk. With
respect to forecasting evaluation, the results indicate that clearly, symmetric GARCH model completely
dominates the others in Kenya, while both GARCH and EGARCH best capture the Tunisian market.
