Markov Switching in GARCH Processes and Mean-Reverting Stock-Market Volatility
AbstractThis 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.
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Bibliographic InfoArticle provided by American Statistical Association in its journal Journal of Business and Economic Statistics.
Volume (Year): 15 (1997)
Issue (Month): 1 (January)
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Other versions of this item:
- Michael Dueker, 1995. "Markov switching in GARCH processes and mean reverting stock market volatility," Working Papers 1994-015, Federal Reserve Bank of St. Louis.
- Tom Doan, . "RATS programs to replicate Dueker(1997) Markov switching GARCH models," Statistical Software Components RTZ00048, Boston College Department of Economics.
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