A flexible observed factor model with separate dynamics for the factor volatilities and their correlation matrix

Abstract

In this article, we consider a novel regression model with observed factors. To allow for the prediction of future observations, we model the observed factors using a flexible multivariate stochastic volatility (MSV) structure with separate dynamics for the volatilities and the correlation matrix. The correlation matrix of the factors is time varying, and its evolution is described by an inverse Wishart process. We develop an estimation procedure based on Bayesian Markov chain Monte Carlo methods, which has two major advantages compared to existing methods for similar models in the literature. First, the procedure is computationally more efficient. Second, it can be applied to calculate the predictive distributions for future observations. We compare the proposed model with other multivariate volatility models using Fama-French factors and portfolio weighted return data. The result shows that our model has better predictive performance.