Extreme News Events, Long-Memory Volatility, and Time Varying Risk Premia in Stock Market Returns

25 Pages Posted: 29 Jul 2008 Last revised: 18 Aug 2008

See all articles by Wing H. Chan

Wing H. Chan

Wilfrid Laurier University - School of Business & Economics; City University of Hong Kong (CityU) - Department of Economics & Finance

LiLing Feng

City University of Hong Kong (CityU) - Department of Economics & Finance

Date Written: July 23, 2008

Abstract

This paper proposes a new class of GARCH-jump in mean models to test the presence of time varying risk premia associated with normal and extreme news events. The model allows for a dynamic jump component with autoregressive jump intensity, long-range dependence in volatility dynamics, and volatility in mean structure separately for normal and extreme news events. The results show significant jump risk premia in five stock market index returns. We also find that ignoring the long-memory feature in volatility dynamics leads to false rejection of time varying risk premia.

Keywords: Time Varying Risk Premium, Poisson Jumps, Component GARCH, FIGARCH, Autoregressive Jump Intensity

JEL Classification: C32, C53, G13, F31

Suggested Citation

Chan, Wing H. and Feng, LiLing, Extreme News Events, Long-Memory Volatility, and Time Varying Risk Premia in Stock Market Returns (July 23, 2008). Available at SSRN: https://ssrn.com/abstract=1183173 or http://dx.doi.org/10.2139/ssrn.1183173

Wing H. Chan (Contact Author)

Wilfrid Laurier University - School of Business & Economics ( email )

Waterloo, Ontario N2L 3C5
Canada
519-884-0710, ext. 2773 (Phone)
519-888-1015 (Fax)

City University of Hong Kong (CityU) - Department of Economics & Finance ( email )

83 Tat Chee Avenue
Kowloon
Hong Kong

LiLing Feng

City University of Hong Kong (CityU) - Department of Economics & Finance ( email )

83 Tat Chee Avenue
Kowloon
Hong Kong

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