Markov switching in GARCH processes and mean reverting stock market volatility

This paper introduces four models of conditional heteroskedasticity that contain markov switching parameters to examine their multi-period stock-market volatility forecasts as predictions of options-implied volatilities. The volatility model that best predicts the behavior of the optionsimplied volatilities allows the student-t degrees-of-freedom parameter to switch such that the conditional variance and kurtosis are subject to discrete shifts. The half-life of the most leptokurtic state is estimated to be weak, so expected market volatility reverts to near-normal levels fairly quickly following a spike.