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Bayesian parametric and semiparametric factor models for large realized covariance matrices
Author(s) -
Jin Xin,
Maheu John M.,
Yang Qiao
Publication year - 2019
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.2685
Subject(s) - parametric statistics , wishart distribution , covariance , dirichlet process , factor analysis , dirichlet distribution , nonparametric statistics , inverse , computer science , mathematics , semiparametric model , bayesian probability , econometrics , statistics , mathematical analysis , geometry , multivariate statistics , boundary value problem
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.