Publication Type
Working Paper
Version
publishedVersion
Publication Date
11-2004
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, heavy-tailed 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
Granger causality in volatility, Heavy-tailed distributions, MCMC, Multivariate stochastic volatility, Time-varying correlations
Discipline
Applied Statistics | Econometrics
Research Areas
Econometrics
First Page
1
Last Page
29
Publisher
SMU Economics and Statistics Working Paper Series, No. 23-2004
City or Country
Singapore
Citation
YU, Jun and MEYER, Renate.
Multivariate Stochastic Volatility Models: Bayesian Estimation and Model Comparison. (2004). 1-29.
Available at: https://ink.library.smu.edu.sg/soe_research/819
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Comments
Published in Econometric Reviews, 2006. https://doi.org/10.1080/07474930600713465