Abstract
This paper shows that Bayesian estimation and comparison of multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) and multivariate stochastic volatility (MSV) models with Markov Chain Monte Carlo methods could be straightforwardly and successfully conducted in WinBUGS package. And an algorithm based on the Cholesky decomposition is proposed to set as a prior for a correlation matrix. They are illustrated by applying three types of parsimonious MGARCH and MSV specifications nested in constant conditional correlations to weekly returns of five sector indexes of Shanghai Stock Exchange over the period of 28 June 2004 to 30 June 2008. Empirical results provide evidence for the superior performance of MSV models over MGARCH models and give support to the feasibility of the algorithm we presented. In addition, the estimation results also suggest the significant negative correlation between the persistency and the variability of volatilities in MSV models.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
