Bayesian semiparametric Markov switching stochastic volatility model

Audronė Virbickaitė, Hedibert F. Lopes

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


This paper proposes a novel Bayesian semiparametric stochastic volatility model with Markov switching regimes for modeling the dynamics of the financial returns. The distribution of the error term of the returns is modeled as an infinite mixture of Normals; meanwhile, the intercept of the volatility equation is allowed to switch between two regimes. The proposed model is estimated using a novel sequential Monte Carlo method called particle learning that is especially well suited for state-space models. The model is tested on simulated data and, using real financial times series, compared to a model without the Markov switching regimes. The results show that including a Markov switching specification provides higher predictive power for the entire distribution, as well as in the tails of the distribution. Finally, the estimate of the persistence parameter decreases significantly, a finding consistent with previous empirical studies.

Original languageEnglish (US)
Pages (from-to)978-997
Number of pages20
JournalApplied Stochastic Models in Business and Industry
Issue number4
StatePublished - Jul 2019
Externally publishedYes


  • Bayes factor
  • Dirichlet process mixture
  • particle learning
  • sequential Monte Carlo

ASJC Scopus subject areas

  • Modeling and Simulation
  • General Business, Management and Accounting
  • Management Science and Operations Research


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