Multivariate Stochastic Volatility

Author: 
Manabu Asai
Michael McAleer
Jun Yu
Description: 
SMU ECONOMICS & STATISTICS WORKING PAPER SERIES Paper No. 33-2006
JEL codes: 
Organisation: 
Abstract: 
The literature on multivariate stochastic volatility (MSV) models has developed significantly over the last few years. This paper reviews the substantial literature on specification, estimation and evaluation of MSV models. A wide range of MSV models is presented according to various categories, namely (i) asymmetric models; (ii) factor models; (iii) time-varying correlation models; and (iv) alternative MSV specifications, including models based on the matrix exponential transformation, Cholesky decomposition, Wishart autoregressive process, and the empirical range. Alternative methods of estimation, including quasi-maximum likelihood, simulated maximum likelihood, Monte Carlo likelihood, and Markov chain Monte Carlo methods, are discussed and compared. Various methods of diagnostic checking and model comparison are also examined.