Multivariate Stochastic Volatility Models: Bayesian Estimation and Model Comparison
展示如何用免费贝叶斯软件WinBUGS轻松实现多元随机波动率模型的估计与比较，并引入时变相关系数等新模型，通过对汇率数据的实证分析发现时变相关系数模型表现最佳。
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.