Bayesian Parametric and Semiparametric Factor Models for Large Realized Covariance Matrices

Xin Jin, John Maheu and Qiao Yang

MPRA Paper from University Library of Munich, Germany

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 infinite hidden Markov mixture which leads to an infinite 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: infinite hidden Markov model; Dirichlet process mixture; inverse-Wishart; predictive density; high-frequency data (search for similar items in EconPapers)
JEL-codes: C11 C14 C32 C58 G17 (search for similar items in EconPapers)
Date: 2017-10-12
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Working Paper: Bayesian Parametric and Semiparametric Factor Models for Large Realized Covariance Matrices (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:81920

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