Simulation-based sequential analysis of Markov switching stochastic volatility models

Carlos M. Carvalho, Hedibert F. Lopes

Research output: Contribution to journalArticlepeer-review

72 Scopus citations


We propose a simulation-based algorithm for inference in stochastic volatility models with possible regime switching in which the regime state is governed by a first-order Markov process. Using auxiliary particle filters we developed a strategy to sequentially learn about states and parameters of the model. The methodology is tested against a synthetic time series and validated with a real financial time series: the IBOVESPA stock index (São Paulo Stock Exchange).

Original languageEnglish (US)
Pages (from-to)4526-4542
Number of pages17
JournalComputational Statistics and Data Analysis
Issue number9
StatePublished - May 15 2007
Externally publishedYes


  • Bayes factor
  • Bayesian time series
  • Markov chain Monte Carlo
  • Particle filters
  • Sequential analysis
  • Stochastic volatility models

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics


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