Bayesian Semiparametric Modeling of Realized Covariance Matrices

Xin Jin () and John Maheu

MPRA Paper from University Library of Munich, Germany

Abstract: This paper introduces several new Bayesian nonparametric models suitable for capturing the unknown conditional distribution of realized covariance (RCOV) matrices. Existing dynamic Wishart models are extended to countably infinite mixture models of Wishart and inverse-Wishart distributions. In addition to mixture models with constant weights we propose models with time-varying weights to capture time dependence in the unknown distribution. Each of our models can be combined with returns to provide a coherent joint model of returns and RCOV. The extensive forecast results show the new models provide very significant improvements in density forecasts for RCOV and returns and competitive point forecasts of RCOV.

Keywords: multi-period density forecasts; inverse-Wishart distribution; beam sampling; hierarchical Dirichlet process; infinite hidden Markov model (search for similar items in EconPapers)
JEL-codes: C11 C14 C32 C58 G17 (search for similar items in EconPapers)
Date: 2014-11
New Economics Papers: this item is included in nep-ecm, nep-for 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:
Journal Article: Bayesian semiparametric modeling of realized covariance matrices (2016)
Working Paper: Bayesian Semiparametric Modeling of Realized Covariance Matrices (2014)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:60102

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