Bayesian Parametric and Semiparametric Factor Models for Large Realized Covariance Matrices

36 Pages Posted: 10 Apr 2018 Last revised: 8 Nov 2018

See all articles by Xin Jin

Xin Jin

Shanghai University of Finance and Economics - School of Economics

John M. Maheu

McMaster University - Michael G. DeGroote School of Business; RCEA

Qiao Yang

ShanghaiTech University - School of Entrepreneurship and Management

Date Written: September 1, 2017

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 infi nite hidden Markov mixture which leads to an in finite 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: in finite hidden Markov model, Dirichlet process mixture, inverse-Wishart, predictive density, high-frequency data

JEL Classification: G17, C11, C14, C32, C58

Suggested Citation

Jin, Xin and Maheu, John M. and Yang, Qiao, Bayesian Parametric and Semiparametric Factor Models for Large Realized Covariance Matrices (September 1, 2017). ShanghaiTech SEM Working Paper No. 2018-005. Available at SSRN: https://ssrn.com/abstract=3159716 or http://dx.doi.org/10.2139/ssrn.3159716

Xin Jin

Shanghai University of Finance and Economics - School of Economics ( email )

777 Guoding Road
Shanghai, 200433
China

John M. Maheu

McMaster University - Michael G. DeGroote School of Business ( email )

1280 Main Street West
Hamilton, Ontario L8S 4M4
Canada

HOME PAGE: http://profs.degroote.mcmaster.ca/ads/maheujm/

RCEA

Via Patara, 3
Rimini (RN), RN 47900
Italy

HOME PAGE: http://www.rcfea.org/

Qiao Yang (Contact Author)

ShanghaiTech University - School of Entrepreneurship and Management ( email )

100 Haike Rd
Pudong Xinqu, Shanghai
China

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