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Bayesian Estimation of Wishart Autoregressive Stochastic Volatility Model

Author

Listed:
  • Ming Lin
  • Changjiang Liu
  • Linlin Niu

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.

Suggested Citation

  • Ming Lin & Changjiang Liu & Linlin Niu, 2013. "Bayesian Estimation of Wishart Autoregressive Stochastic Volatility Model," Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    • Handle: RePEc:wyi:wpaper:002054
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    References listed on IDEAS

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    More about this item

    Keywords

    Bayesian posterior probability; Markov chain Monte Carlo; Multivariate stochastic volatility; Sequential Monte Carlo; Wishart autoregressive process;
    All these keywords.

    JEL classification:

    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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