Multivariate Stochastic Volatility Models: Bayesian Estimation and Model Comparison

Jun Yu and Renate Meyer ()
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Renate Meyer: University of Auckland

No 23-2004, Working Papers from Singapore Management University, School of Economics

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 which 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, heavytailed error distributions, additive factor structure, and multiplicative factor structure. Empirical results suggest that the most adequate specifications are those that allow for time varying correlation coefficients.

Keywords: Multivariate stochastic volatility; Granger causality in volatility; Heavy-tailed distributions; Time varying correlations; Factors; MCMC; DIC. (search for similar items in EconPapers)
JEL-codes: C11 C15 C30 G12 (search for similar items in EconPapers)
Pages: 30 pages
Date: 2004-11
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-fin and nep-sea
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Published in SMU Economics and Statistics Working Paper Series

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Related works:
Journal Article: Multivariate Stochastic Volatility Models: Bayesian Estimation and Model Comparison (2006)
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