ShanghaiTech SEM Working Paper No. 2018-005
Xin Jin
Shanghai University of Finance and Economics - School of Economics
John M. Maheu
McMaster University - DeGroote School of Business
Qiao Yang
ShanghaiTech University - School of Entrepreneurship and Management
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 in nite hidden Markov mixture which leads to an in nite 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 nite hidden Markov model, Dirichlet process mixture, inverse-Wishart, predictive density, high-frequency data
JEL Classification: G17, C11, C14, C32, C58
Date Written: September 2017
Available at SSRN: http://ssrn.com/abstract=3159716
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