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.
DIC, Factors, Granger causality in volatility, Heavy-tailed distributions, MCMC, Multivariate stochastic volatility, Time-varving correlations
Taylor and Francis
YU, Jun and MEYER, Renate.
Multivariate Stochastic Volatility Models: Bayesian Estimation and Model Comparison. (2006). Econometric Reviews. 25, (2/3), 361-384. Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/360
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