Bayesian parametric and semiparametric factor models for large realized covariance matrices

摘要

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. Because of 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 assets and 60 assets show that the models perform well. By exploiting parallel computing the models can be estimated in a matter of a few minutes.