An Infinite Hidden Markov Model with Stochastic Volatility

Chenxing Li, John Maheu and Qiao Yang

MPRA Paper from University Library of Munich, Germany

Abstract: This paper extends the Bayesian semiparametric stochastic volatility (SV-DPM) model of Jensen and Maheu (2010). Instead of using a Dirichlet process mixture (DPM) to model return innovations, we use an infinite hidden Markov model (IHMM). This allows for time variation in the return density beyond that attributed to parametric latent volatility. The new model nests several special cases as well as the SV-DPM. We also discuss posterior and predictive density simulation methods for the model. Applied to equity returns, foreign exchange rates, oil price growth and industrial production growth, the new model improves density forecasts, compared to the SV-DPM, a stochastic volatility with Student-t innovations and other fat-tailed volatility models.

Keywords: stochastic volatility; Markov-switching; MCMC; Bayesian; nonparametric; semiparametric (search for similar items in EconPapers)
JEL-codes: C11 C14 C22 C53 C58 (search for similar items in EconPapers)
Date: 2022-11-25
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-rmg and nep-sea
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Related works:
Journal Article: An infinite hidden Markov model with stochastic volatility (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:115456

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