The purpose of this paper is to evaluate the forecasting performance of linear and
non-linear (GARCH) models in terms of their in-sample and out-of-sample forecasting
accuracy for EGX30 and Nikkei225 indices as an example of an emerging and developed
markets respectively.
We employ GARCH, GARCH-IN-MEAN, EGARCH, GJR-GARCH, Multivariate
GARCH, and Nelson's EGARCH for forecasting using daily price data of the indices for
the period of 2001 to 2019. We find that the volatility shocks on the indices returns are
quite persistent. Furthermore, our findings show that the indices have leverage effect, and
the impact of shocks is asymmetric, and consequently it can be stated that the impact of
negative shocks on volatility are higher than positive shocks.
The results suggest that the Nelson's EGARCH model is the most accurate model in
the GARCH class for forecasting, as this model outperforms the other models.
Additionally, we find that emerging stock markets have higher volatilities than those in
developed markets. Further, these results imply that the EGARCH model might be more
useful than other models when implementing risk management strategies and developing
stock pricing model.
This paper contributes to the literature by comparing two significant global markets;
one of the largest developed economies in the world, Japan, and one of Africa’s largest
developing economies, Egypt
