Publication Type
Journal Article
Version
acceptedVersion
Publication Date
9-2006
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 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.
Keywords
DIC, Factors, Granger causality in volatility, Heavy-tailed distributions, MCMC, Multivariate stochastic volatility, Time-varving correlations
Discipline
Econometrics
Research Areas
Econometrics
Publication
Econometric Reviews
Volume
25
Issue
2/3
First Page
361
Last Page
384
ISSN
0747-4938
Identifier
10.1080/07474930600713465
Publisher
Taylor and Francis
Citation
YU, Jun and MEYER, Renate.
Multivariate Stochastic Volatility Models: Bayesian Estimation and Model Comparison. (2006). Econometric Reviews. 25, (2/3), 361-384.
Available at: https://ink.library.smu.edu.sg/soe_research/360
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.
Additional URL
https://doi.org/10.1080/07474930600713465