Extreme News Events, Long-memory Volatility, and Time Varying Risk Premia in Stock Market Returns
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چکیده
This paper proposes a new GARCH-jump in mean model 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 a volatility in mean structure separately for the normal and extreme news events. The results show significant jump risk premia in five stock market index returns including the DAX, DJIA, FTSE, Nikkei, and S&P500 indices. We also find ignoring the long-memory feature in volatility dynamics leads to false rejection of time varying risk premia.
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