Munich Personal RePEc Archive

Bayesian Parametric and Semiparametric Factor Models for Large Realized Covariance Matrices

Jin, Xin and Maheu, John M and Yang, Qiao (2017): Bayesian Parametric and Semiparametric Factor Models for Large Realized Covariance Matrices.

[img]
Preview
PDF
MPRA_paper_81920.pdf

Download (533kB) | Preview

Abstract

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.

UB_LMU-Logo
MPRA is a RePEc service hosted by
the Munich University Library in Germany.