Multivariate Stochastic Volatility: A Review

Manabu Asai
Michael McAleer
Jun Yu, Singapore Management University


The literature on multivariate stochastic volatility (MSV) models has developed significantly over the last four 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, the Cholesky decomposition, and the Wishart autoregressive process. Alternative methods of estimation, including quasi-maximum likelihood, simulated maximum likelihood, and Markov chain Monte Carlo methods, are discussed and compared. Various methods of diagnostic checking and model comparison are also reviewed. [ABSTRACT FROM AUTHOR]