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An infinite hidden Markov model with stochastic volatility

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  • Chenxing Li
  • John M. Maheu
  • Qiao Yang

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.

Suggested Citation

  • Chenxing Li & John M. Maheu & Qiao Yang, 2024. "An infinite hidden Markov model with stochastic volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 2187-2211, September.
    • Handle: RePEc:wly:jforec:v:43:y:2024:i:6:p:2187-2211
    DOI: 10.1002/for.3123
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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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