Bayesian Estimation of Wishart Autoregressive Stochastic Volatility Model

Ming Lin, Changjiang Liu and Linlin Niu

No 2013-10-14, Working Papers from Wang Yanan Institute for Studies in Economics (WISE), Xiamen University

Abstract: 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.

Keywords: Bayesian posterior probability; Markov chain Monte Carlo; Multivariate stochastic volatility; Sequential Monte Carlo; Wishart autoregressive process (search for similar items in EconPapers)
JEL-codes: C11 C58 G13 G17 (search for similar items in EconPapers)
Date: 2013-10-14
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:wyi:wpaper:002054

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