An infinite hidden Markov model with stochastic volatility

Chenxing Li, John Maheu and Qiao Yang

Journal of Forecasting, 2024, vol. 43, issue 6, 2187-2211

Abstract: This paper extends the Bayesian semiparametric stochastic volatility (SV‐DPM) model. 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 with the SV‐DPM, a stochastic volatility with Student's t innovations and other fat‐tailed volatility models.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://doi.org/10.1002/for.3123

Related works:
Working Paper: An Infinite Hidden Markov Model with Stochastic Volatility (2022)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:43:y:2024:i:6:p:2187-2211

Access Statistics for this article

Journal of Forecasting is currently edited by Derek W. Bunn

More articles in Journal of Forecasting from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().