Bayesian parametric and semiparametric factor models for large realized covariance matrices

Xin Jin, John Maheu and Qiao Yang

Journal of Applied Econometrics, 2019, vol. 34, issue 5, 641-660

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. 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.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (15)

Downloads: (external link)
https://doi.org/10.1002/jae.2685

Related works:
Working Paper: Bayesian Parametric and Semiparametric Factor Models for Large Realized Covariance Matrices (2018)
Working Paper: Bayesian Parametric and Semiparametric Factor Models for Large Realized Covariance Matrices (2017)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:japmet:v:34:y:2019:i:5:p:641-660

Ordering information: This journal article can be ordered from
http://www3.intersci ... e.jsp?issn=0883-7252

Access Statistics for this article

Journal of Applied Econometrics is currently edited by M. Hashem Pesaran

More articles in Journal of Applied Econometrics from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().