Multivariate Stochastic Volatility Models: Bayesian Estimation and Model Comparison

Author

Jun YU, Singapore Management UniversityFollow
Renate MEYER, University of Auckland

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