The Use of Standardized Exponentiated Gumbel Error Innovation Distribution to Forecast Volatility: A Comparative Study

Abstract

This study is designed to model several selected volatility models using a newly developed error innovation distribution called Standardized Exponentiated Gumbel Error Innovation Distribution (SEGEID) to determine the efficiency and effectiveness of the model in terms of its adaptability and forecast evaluation. SEGEID improves some existing error distributions and uses the standard&Poor-500 index data returned from 2004 to 2022.The use of this error innovation distribution, GJR-GARCH (1,1), has been shown to be more effective than other volatility models considered in this study. The results of the study show that GJR-GARCH (1,1) is better than GARCH (1,1), EGARCH (1,1) and TGARCH (1, 1) because it has the lowest AIC and RMSE.