Markov Switching in GARCH Processes and Mean-Reverting Stock-Market Volatility
提出四种含马尔可夫转换参数的条件异方差模型，用它们预测期权隐含波动率，发现允许t分布自由度转换的模型效果最好，且尖峰状态的半衰期约为一周，市场波动率在冲击后快速回复正常。
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.