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

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Author Info

  • Tsunehiro Ishihara

    (Graduate School of Economics, University of Tokyo)

  • Yasuhiro Omori

    (Faculty of Economics, University of Tokyo)

Abstract

The efficient Bayesian estimation method using Markov chain Monte Carlo is proposed for a multivariate stochastic volatility model that is a natural extension of the univariate stochastic volatility model with leverage and heavy-tailed errors, where we further incorporate cross leverage effects among stock returns. Our method is based on a multi-move sampler which samples a block of latent volatility vectors and is described first in the literature for a multivariate stochastic volatility model with cross leverage and heavy-tailed errors. Its high sampling efficiency is shown using numerical examples in comparison with a single-move sampler which samples one latent volatility vector at a time given other latent vectors and parameters. The empirical studies are given using five dimensional stock return indices in Tokyo Stock Exchange.

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Bibliographic Info

Paper provided by CIRJE, Faculty of Economics, University of Tokyo in its series CIRJE F-Series with number CIRJE-F-700.

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Length: 29pages
Date of creation: Dec 2009
Date of revision:
Handle: RePEc:tky:fseres:2009cf700

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Cited by:
  1. Michael McAleer & Massimiliano Caporin, 2011. "Ranking Multivariate GARCH Models by Problem Dimension:An Empirical Evaluation," KIER Working Papers 778, Kyoto University, Institute of Economic Research.

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