Bayesian Parametric and Semiparametric Factor Models for Large Realized Covariance Matrices
36 Pages Posted: 10 Apr 2018 Last revised: 8 Nov 2018
Date Written: September 1, 2017
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 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.
Keywords: infinite hidden Markov model, Dirichlet process mixture, inverse-Wishart, predictive density, high-frequency data
JEL Classification: G17, C11, C14, C32, C58
Suggested Citation: Suggested Citation