Our assistant professor, Prof. Qiao Yang has published his recent joint work, “Bayesian Parametric and Semiparametric Factor Models for Large Realized Covariance Matrices” at Journal of Applied Econometrics. His coauthors are Xin Jin and John Maheu.
Their 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 assets and 60 assets show the models perform well. By exploiting parallel computing the models can be estimated in a matter of a few minutes.