Multivariate Stochastic Volatility Models: Bayesian Estimation and Model Comparison

Jun Yu and Renate Meyer

Econometric Reviews, 2006, vol. 25, issue 2-3, 361-384

Abstract: In this paper we show that fully likelihood-based estimation and comparison of multivariate stochastic volatility (SV) models can be easily performed via a freely available Bayesian software called WinBUGS. Moreover, we introduce to the literature several new specifications that are natural extensions to certain existing models, one of which allows for time-varying correlation coefficients. Ideas are illustrated by fitting, to a bivariate time series data of weekly exchange rates, nine multivariate SV models, including the specifications with Granger causality in volatility, time-varying correlations, heavy-tailed error distributions, additive factor structure, and multiplicative factor structure. Empirical results suggest that the best specifications are those that allow for time-varying correlation coefficients.

Keywords: DIC; Factors; Granger causality in volatility; Heavy-tailed distributions; MCMC; Multivariate stochastic volatility; Time-varying correlations (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (80)

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Working Paper: Multivariate Stochastic Volatility Models: Bayesian Estimation and Model Comparison (2004)
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DOI: 10.1080/07474930600713465

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