A Stochastic Volatility Model With Markov Switching
提出一种结合马尔可夫转换的随机波动率模型，用吉布斯采样进行贝叶斯估计，在标普500周收益率数据中识别出高、中、低三种波动状态，高波动状态能捕捉1987年股灾并与四次美国经济衰退期高度重叠。
This article presents a new way of modeling time-varying volatility. We generalize the usual stochastic volatility models to encompass regime-switching properties. The unobserved state variables are governed by a first-order Markov process. Bayesian estimators are constructed by Gibbs sampling. High-, medium- and low-volatility states are identified for the Standard and Poor’s 500 weekly return data. Persistence in volatility is explained by the persistence in the low- and the medium-volatility states. The high-volatility regime is able to capture the 1987 crash and overlap considerably with four U.S. economic recession periods.