Dynamic Correlations in Symmetric Multivariate SV Models

This paper proposes two types of stochastic correlation structures for Multivariate Stochastic Volatility (MSV) models, namely the constant correlation (CC) MSV and dynamic correlation (DC) MSV models, from which the stochastic covariance structures could be obtained easily. Both structures can be used for purposes of optimal portfolio and risk management, and for calculating Value-at-Risk (VaR) forecasts and optimal capital charges under the Basel Accord. The choice between the CC MSV and DC MSV models can be made using a deviance information criterion. A technique is developed to estimate the DC MSV model using the Markov Chain Monte Carlo (MCMC) procedure.