Statistical Modelling 14 (1) (2014), 1–20
A flexible observed factor model with separate dynamics for the factor volatilities and their correlation matrix
Yu-Cheng Ku
Enterprise Risk Management,
Fannie Mae,
Washington,
DC 20016,
USA
e-mail: yku2@ncsu.edu
Peter Bloomfield
Department of Statistics,
North Carolina State University,
Raleigh,
NC 27695,
USA
Sujit K. Ghosh
Department of Statistics,
North Carolina State University,
Raleigh,
NC 27695,
USA
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.
Keywords:
correlated factors; time-varying covariance; inverse Wishart; Markov chain Monte Carlo; stochastic volatility
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