Short Memory, Long Memory and Jump Dynamics in Global Financial Markets
Journal
財務金融學刊
Journal Volume
15
Journal Issue
2
Pages
43-68
Date Issued
2007-06
Author(s)
Chih-Chiang Hsu
Abstract
This study evaluates the performance of alternative volatility models, including EGARCH, FIEGARCH, EGARCH-jump, and EGARCH-skewed-t models, on model fitting, volatility forecasting, and Value-at-Risk (VaR) prediction. As compared with the simple EGARCH model, the EGARCH-jump model demonstrates significant improvements, outperforming every other model in almost all aspects, but the computation load substantially increases by the inclusion of jump dynamics. If less expensive volatility models are preferred, then alternatives may include the use of the EGARCH-skewed-t model for model fitting and VaR prediction, and the FIEGARCH model for volatility forecasting, since these models also demonstrate fairly good performance for these specific purposes. Furthermore, since the FIEGARCH model demonstrates relatively good performance with regard to U.S. stock market returns only, this suggests that the long-memory pattern captured by the fractionally-integrated volatility model may not be a global stylized fact.
SDGs
Type
journal article