Bayesian Parametric and Semiparametric Factor Models for Large Realized Covariance Matrices

This paper introduces a new factor structure suitable for modeling large realized covariance matrices with full likelihood based estimation. Parametric and nonparametric versions are introduced. Due to the computational advantages of our approach, we can model the factor nonparametrically as a Dirichlet process mixture or as an infinite hidden Markov mixture which leads to an infinite mixture of inverse-Wishart distributions. Applications to 10 …