Multivariate Stochastic Volatility Models: Bayesian Estimation and Model Comparison

• Main Authors: YU, Jun, MEYER, Renate
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2006
• Subjects: DIC; Factors; Granger causality in volatility; Heavy-tailed distributions; MCMC; Multivariate stochastic volatility; Time-varving correlations; Econometrics
• Online Access: https://ink.library.smu.edu.sg/soe_research/360; https://ink.library.smu.edu.sg/context/soe_research/article/1359/viewcontent/Multivariate_Stochastic_Volatility_Models_Bayesian_Estimation.pdf
• Institution: Singapore Management University

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.