Efficient Bayesian Estimation of a Multivariate Stochastic Volatility Model with Cross Leverage and Heavy-Tailed Errors

Tsunehiro Ishihara and Yasuhiro Omori ()

No CARF-F-221, CARF F-Series from Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo

Abstract: An efficient Bayesian estimation using a Markov chain Monte Carlo method is proposed in the case of a multivariate stochastic volatility model as a natural extension of the univariate stochastic volatility model with leverage and heavy-tailed errors. Note that we further incorporate cross-leverage effects among stock returns. Our method is based on a multi-move sampler that samples a block of latent volatility vectors. The method is presented as a multivariate stochastic volatility model with cross leverage and heavytailed errors. Its high sampling efficiency is shown using numerical examples in comparison with a single-move sampler that samples one latent volatility vector at a time, given other latent vectors and parameters. To illustrate the method, empirical analyses are provided based on five-dimensional S&P500 sector indices returns.

Pages: 31 pages
Date: 2010-05
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Citations: View citations in EconPapers (5)

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Related works:
Journal Article: Efficient Bayesian estimation of a multivariate stochastic volatility model with cross leverage and heavy-tailed errors (2012)
Working Paper: Efficient Bayesian Estimation of a Multivariate Stochastic Volatility Model with Cross Leverage and Heavy-Tailed Errors (2010)
Working Paper: Efficient Bayesian estimation of a multivariate stochastic volatility model with cross leverage and heavy-tailed errors (2009)
Working Paper: Efficient Bayesian Estimation of a Multivariate Stochastic Volatility Model with Cross Leverage and Heavy-Tailed Errors (2009)
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