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，用期权隐含波动率检验其多期预测能力，发现允许学生t分布自由度参数转换的模型最佳，且尖峰态状态半衰期短，市场波动率在冲击后快速回复正常水平。
This paper introduces four models of conditional heteroscedasticity 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.