Markov Switching in GARCH Processes and Mean-Reverting Stock-Market Volatility

Michael Dueker

Journal of Business & Economic Statistics, 1997, vol. 15, issue 1, 26-34

Abstract: This article introduces four models of conditional heteroscedasticity that contain Markov-switching parameters to examine their multiperiod stock-market volatility forecasts as predictions of options-implied volatilities. The volatility model that best predicts the behavior of the options-implied 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 a week, so expected market volatility reverts to near-normal levels fairly quickly following a spike.

Date: 1997
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Related works:
Working Paper: Markov switching in GARCH processes and mean reverting stock market volatility (1995)
Software Item: RATS programs to replicate Dueker(1997) Markov switching GARCH models
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