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

淡江大學機構典藏 > 商管學院 > 財務金融學系暨研究所 > 期刊論文 > Item 987654321/126744

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题名:	Forecasting S&P-100 stock index volatility: The role of volatility asymmetry and distributional assumption in GARCH models
作者:	Hung, Jui-cheng
关键词:	Volatility;GARCH;Asymmetry;Distribution;SPA test
日期:	2009-12-09
上传时间:	2025-03-20 09:22:32 (UTC+8)
出版者:	Elsevier
摘要:	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.
關聯:	Expert Systems with Applications 37(7),　p.4928-4934
DOI:	10.1016/j.eswa.2009.12.022
显示于类别:	[財務金融學系暨研究所] 期刊論文

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