AcademicsWorking Papers

Bayesian Estimation of Wishart Autoregressive Stochastic Volatility Model
Ming Lin, Changjiang Liu, Linlin Niu
#002054 20131014 (published)
The Wishart autoregressive (WAR) process is a powerful tool to model multivariate stochastic volatility (MSV) with correlation risk and derive closed-form solutions in various asset pricing models. However, making inferences of the WAR stochastic volatility (WAR-SV) model is challenging because the latent volatility series does not have a closed-form transition density. Based on an alternative representation of the WAR process with lag order p=1 and integer degrees of freedom, we develop an effective two-step procedure to estimate parameters and the latent volatility series. The procedure can be applied to study other varying-dimension problems. We show the effectiveness of this procedure with a simulated example. Then this method is used to study the time-varying correlation of US and China stock market returns.
JEL-Codes: G13, G17, C11, C58
Keywords: Bayesian posterior probability, Markov chain Monte Carlo, Multivariate stochastic volatility, Sequential Monte Carlo, Wishart autoregressive process

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