A Stochastic Volatility Model with Markov Switching
This article presents a new way of modeling time-varying volatility. The authors 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.
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Volume (Year): 16 (1998)
Issue (Month): 2 (April)
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