An infinite hidden Markov model with stochastic volatility
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 ra… Show more
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