A stochastic volatility model with Markov switching

Mike K. P. SO, K. LAM, Wai Keung LI

Research output: Contribution to journalArticles

102 Citations (Scopus)

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. Copyright © 1998 American Statistical Association.
Original languageEnglish
Pages (from-to)244-253
JournalJournal of Business and Economic Statistics
Volume16
Issue number2
DOIs
Publication statusPublished - Apr 1998

Citation

So, M. K. P., Lam, K., & Li, W. K. (1998). A stochastic volatility model with Markov switching. Journal of Business & Economic Statistics, 16(2), 244-253. doi: 10.1080/07350015.1998.10524758

Keywords

  • ARCH model
  • Bayesian inference
  • Data augmentation
  • Gibbs sampling
  • Monte Carlo Markov chain

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