forecasting volatility model with the most appropriate error distribution. The results suggest the presence of leverage effect … validate this result. The last twenty eight days out-of-sample forecast adjudged Power-GARCH (1, 1, 1) in student's t error … forecasting model that could guarantee a sound policy decisions. …

-day forecasting horizon, these findings are confirmed by value-at-risk forecasts …

Alternative strategies for predicting stock market volatility are examined. In out-of-sample forecasting experiments …, such as GARCH models, are investigated, to determine if they are more appropriate for predicting future return volatility …

squared forecast error (MSE) were used to compare the forecasting ability of the ex-ante GARCH models, Artificial Neural … (GARCH) models, of differing lag and parameter terms, to forecast the variance of the market used in the denominator of the …

-memory counterparts. Since long memory models should have a particular advantage over long forecasting horizons, we consider predictions … multifractal model) dominate over GARCH and ARMA models. However, while FIGARCH and ARFIMA also have quite a number of cases with …

-memory counterparts. Since long memory models should have a particular advantage over long forecasting horizons, we consider predictions … multifractal models) dominate over GARCH and ARMA models. However, while FIGARCH and ARFIMA also have a number of cases with …

2010 to 2016. The results indicate that the model is robust in explaining and forecasting quarterly returns of individual …