Forecasting S&P-100 stock index volatility: The role of volatility asymmetry and distributional assumption in GARCH models
學年 98
學期 1
出版（發表）日期 2009-12-09
作品名稱 Forecasting S&P-100 stock index volatility: The role of volatility asymmetry and distributional assumption in GARCH models
作品名稱（其他語言）
著者 Hung, Jui-cheng
單位
出版者
著錄名稱、卷期、頁數 Expert Systems with Applications 37(7),　p.4928-4934
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.
關鍵字 Volatility;GARCH;Asymmetry;Distribution;SPA test
語言 en
ISSN 1873-6793
期刊性質 國外
收錄於 SCI NotTSSCI
產學合作
通訊作者
審稿制度 是
國別 USA
公開徵稿
出版型式 ,電子版