Markov switching in GARCH processes and mean reverting stock market volatility
AbstractThis 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.
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Bibliographic InfoPaper provided by Federal Reserve Bank of St. Louis in its series Working Papers with number 1994-015.
Date of creation: 1995
Date of revision:
Publication status: Published in Journal of Business and Economic Statistics, January 1997
Other versions of this item:
- Dueker, Michael J, 1997. "Markov Switching in GARCH Processes and Mean-Reverting Stock-Market Volatility," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 26-34, January.
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