Bayesian parametric and semiparametric factor models for large realized covariance matrices

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. Because of the computational advantages of our approach, we can model the factor nonparametrically as a D...

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Bibliographic Details
Published in:Journal of applied econometrics (Chichester, England) England), 2019-08, Vol.34 (5), p.641-660
Main Authors: Jin, Xin, Maheu, John M., Yang, Qiao
Format: Article
Language:English
Subjects:
Assets
Bayesian analysis
Covariance matrix
Dirichlet problem
Econometrics
Markov processes
Matrices
RESEARCH ARTICLE
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Summary: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. Because of 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 that the models perform well. By exploiting parallel computing the models can be estimated in a matter of a few minutes.
ISSN:0883-7252
1099-1255
DOI:10.1002/jae.2685