Estimation and Empirical Performance of Non-Scalar Dynamic Conditional Correlation Models

Forthcoming in Computational Statistics and Data Analysis

68 Pages Posted: 11 Mar 2014 Last revised: 21 Feb 2015

See all articles by Luc Bauwens

Luc Bauwens

Université catholique de Louvain

Lyudmila Grigoryeva

University of Konstanz

Juan-Pablo Ortega

Centre National de la Recherche Scientifique (CNRS); Nanyang Technological University

Date Written: March 11, 2014

Abstract

A method capable of estimating richly parametrized versions of the dynamic conditional correlation (DCC) model that go beyond the standard scalar case is presented. The algorithm is based on the maximization of a Gaussian quasi-likelihood using a Bregman-proximal trust-region method that handles the various non-linear stationarity and positivity constraints that arise in this context. The general matrix Hadamard DCC model with full rank, rank equal to two and, additionally, two different rank one matrix specifications are considered. In the last mentioned case, the elements of the vectors that determine the rank one parameter matrices are either arbitrary or parsimoniously defined using the Almon lag function. Actual stock returns data in dimensions up to thirty are used in order to carry out performance comparisons according to several in- and out-of-sample criteria. Empirical results show that the use of richly parametrized models adds value with respect to the conventional scalar case.

Keywords: multivariate volatility modeling, dynamic conditional correlations (DCC), non-scalar DCC models, constrained optimization, Bregman divergences, Bregman-proximal trust-region method.

JEL Classification: C13, C32, G17.

Suggested Citation

Bauwens, Luc and Grigoryeva, Lyudmila and Ortega, Juan-Pablo and Ortega, Juan-Pablo, Estimation and Empirical Performance of Non-Scalar Dynamic Conditional Correlation Models (March 11, 2014). Forthcoming in Computational Statistics and Data Analysis, Available at SSRN: https://ssrn.com/abstract=2407652 or http://dx.doi.org/10.2139/ssrn.2407652

Luc Bauwens

Université catholique de Louvain ( email )

CORE
34 Voie du Roman Pays
B-1348 Louvain-la-Neuve, b-1348
Belgium
32 10 474321 (Phone)
32 10 474301 (Fax)

Lyudmila Grigoryeva

University of Konstanz ( email )

Fach D-144
Universitätsstraße 10
Konstanz, D-78457
Germany

Juan-Pablo Ortega (Contact Author)

Centre National de la Recherche Scientifique (CNRS) ( email )

16 route de Gray
Besançon, 25030
France

HOME PAGE: http://juan-pablo-ortega.com

Nanyang Technological University ( email )

21 Nanyang Link
Singapore, 637371
Singapore

HOME PAGE: http://https://juan-pablo-ortega.com

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