EconPapers    
Economics at your fingertips  
 

Estimation and empirical performance of non-scalar dynamic conditional correlation models

Luc Bauwens, Lyudmila Grigoryeva and Juan-Pablo Ortega

Computational Statistics & Data Analysis, 2016, vol. 100, issue C, 17-36

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 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947315000547
Full text for ScienceDirect subscribers only.

Related works:
Working Paper: Estimation and empirical performance of non-scalar dynamic conditional correlation models (2014) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:100:y:2016:i:c:p:17-36

DOI: 10.1016/j.csda.2015.02.013

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu (repec@elsevier.com).

 
Page updated 2024-11-07
Handle: RePEc:eee:csdana:v:100:y:2016:i:c:p:17-36