Forecasting S&P-100 stock index volatility: The role of volatility asymmetry and distributional assumption in GARCH models

Authors:
Hung-Chun Liu;Jui-Cheng Hung
Affiliations:
Department of Finance, Minghsin University of Science and Technology, No. 1, Xinxing Rd., Xinfeng Hsinchu 30401, Taiwan, ROC;Department of Finance, Lunghwa University of Science and Technology, No. 300, Sec. 1, Wanshou Rd., Guishan Shiang, Taoyuan County 33306, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

Citing 1
Cited 2

Financial data and the skewed generalized T distribution

Management Science

A comparison of GARCH models for VaR estimation

Expert Systems with Applications: An International Journal
Application of polynomial projection ensembles to hedging crude oil commodity risk

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.05

Visualization

Abstract

This study investigates the daily volatility forecasting for the Standard & Poor's 100 stock index series from 1997 to 2003 and identifies the essential source of performance improvements between distributional assumption and volatility specification using distribution-type (GARCH-N, GARCH-t, GARCH-HT and GARCH-SGT) and asymmetry-type (GJR-GARCH and EGARCH) volatility models through the superior predictive ability (SPA) test. Empirical results indicate that the GJR-GARCH model achieves the most accurate volatility forecasts, closely followed by the EGARCH model. Such evidence strongly demonstrates that modeling asymmetric components is more important than specifying error distribution for improving volatility forecasts of financial returns in the presence of fat-tails, leptokurtosis, skewness and leverage effects. Furthermore, if asymmetries are neglected, the GARCH model with normal distribution is preferable to those models with more sophisticated error distributions.