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

Tsunehiro Ishihara and Yasuhiro Omori ()

No CIRJE-F-700, CIRJE F-Series from CIRJE, 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.

Pages: 29pages
Date: 2009-12
New Economics Papers: this item is included in nep-ecm
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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)
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Persistent link: https://EconPapers.repec.org/RePEc:tky:fseres:2009cf700

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