A Stochastic Volatility Model With Markov Switching |
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| Authors: | Mike EC. P. So K. Lam W. K. Li |
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| Affiliation: | 1. Department of Information and Systems Management , The Hong Kong University of Science and Technology , Hong Kong E-mail: immkpso@usthk.ust.hk;2. Department of Finance and Decision Sciences , Hong Kong Baptist University , Hong Kong E-mail: lamkin@hkbu.edu.hk;3. Department of Statistics , The University of Hong Kong , Hong Kong E-mail: hrntlwk@hkucc.hku.hk |
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| Abstract: | 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. |
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| Keywords: | ARCH model Bayesian inference Data augmentation Gibbs sampling Monte Carlo Markov chain |
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