Efficient Bayesian estimation of a multivariate stochastic volatility model with cross leverage and heavy-tailed errors

Tsunehiro Ishihara and Yasuhiro Omori ()

Computational Statistics & Data Analysis, 2012, vol. 56, issue 11, 3674-3689

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. The cross-leverage effects are further incorporated among stock returns. The method is based on a multi-move sampler that samples a block of latent volatility vectors. 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 proposed method, empirical analyses are provided based on five-dimensional S&P500 sector indices returns.

Keywords: Asymmetry; Heavy-tailed error; Leverage effect; Markov chain Monte Carlo; Multi-move sampler; Multivariate stochastic volatility (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (24)

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Related works:
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:eee:csdana:v:56:y:2012:i:11:p:3674-3689

DOI: 10.1016/j.csda.2010.07.015

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