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.
Granger causality in volatility; Heavy-tailed distributions; MCMC; Multivariate stochastic volatility; Time-varving correlations
Applied Statistics | Econometrics
YU, Jun and Meyer, Renate.
Multivariate Stochastic Volatility Models: Bayesian Estimation and Model Comparison. (2004). Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/819
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