Bayesian unit-root testing in stochastic volatility models

Mike K. P. SO, Wai Keung LI

Research output: Contribution to journalArticlespeer-review

15 Citations (Scopus)


This article uses a Bayesian unit-root test in stochastic volatility models. The time series of interest is the volatility that is unobservable. The unit-root testing is based on the posterior odds ratio, which is approximated by Markov-chain Monte Carlo methods. Simulations show that the testing procedure is efficient for moderate sample size. The unit-root hypothesis is rejected in seven market indexes, and some evidence of nonstationarity is observed in the TWSI of Taiwan. Copyright © 1999 American Statistical Association.
Original languageEnglish
Pages (from-to)491-496
JournalJournal of Business and Economic Statistics
Issue number4
Publication statusPublished - 1999


So, M. K. P., & Li, W. K. (1999). Bayesian unit-root testing in stochastic volatility models. Journal of Business & Economic Statistics, 17(4), 491-496. doi: 10.1080/07350015.1999.10524838


  • ARCH model
  • Bayes factor
  • Data augmentation
  • Gibbs sampling
  • Monte Carlo Markov chain
  • Posterior odds ratio


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