An Infinite Hidden Markov Model with Stochastic Volatility

时间:2022-03-21浏览:11设置

ShanghaiTech SEM Working Paper No. 2022-001



Chenxing Li

Hunan University

John M. Maheu

McMaster Univeristy 

Qiao Yang

ShanghaiTech University


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.


Date Written: March 2022

Available at SSRN: https://ssrn.com/abstract=4069359

Download this paper: 【No. 2022-001】An Infinite Hidden Markov Model with Stochastic Volatility.pdf




















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