Multiplicative Conditional Correlation Models for Realized Covariance Matrices

CORE DISCUSSION PAPER SERIES, 2020

29 Pages Posted: 25 Feb 2020

See all articles by Luc Bauwens

Luc Bauwens

Université catholique de Louvain

Manuela Braione

SOSE

Giuseppe Storti

University of Salerno - Department of Economics

Date Written: January 29, 2020

Abstract

We introduce a class of multiplicative dynamic models for realized covariance matrices assumed to be conditionally Wishart distributed. The multiplicative structure enables consistent three-step estimation of the parameters, starting by covariance targeting of a scale matrix. The dynamics of conditional variances and correlations are inspired by specifications akin to the consistent dynamic conditional correlation model of the multivariate GARCH literature, and estimation is performed by quasi maximum likelihood. Simulations show that in finite samples the three-step estimator has smaller bias and root mean squared error than the full estimator when the cross-sectional dimension increases. An empirical application illustrates the flexibility of these models in a low-dimensional setting, and another one illustrates their effectiveness and practical usefulness in high dimensional portfolio allocation strategies.

Suggested Citation

Bauwens, Luc and Braione, Manuela and Storti, Giuseppe, Multiplicative Conditional Correlation Models for Realized Covariance Matrices (January 29, 2020). CORE DISCUSSION PAPER SERIES, 2020, Available at SSRN: https://ssrn.com/abstract=3527455 or http://dx.doi.org/10.2139/ssrn.3527455

Luc Bauwens

Université catholique de Louvain ( email )

CORE
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Manuela Braione (Contact Author)

SOSE ( email )

Via Mentore Maggini
Rome, 00143
Italy
00143 (Fax)

Giuseppe Storti

University of Salerno - Department of Economics ( email )

Via John Paul II, 132
Fisciano (SA), 84084
Italy

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