Bayesian parametric and semiparametric factor models for large realized covariance matrices

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 nonparametricall...

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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
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description 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.
doi_str_mv 10.1002/jae.2685
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source International Bibliography of the Social Sciences (IBSS); Wiley:Jisc Collections:Wiley Read and Publish Open Access 2024-2025 (reading list)
subjects Assets
Bayesian analysis
Covariance matrix
Dirichlet problem
Econometrics
Markov processes
Matrices
title Bayesian parametric and semiparametric factor models for large realized covariance matrices
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