Two Cholesky-log-GARCH models for multivariate volatilities. (June 2015)

• Record Type: Journal Article
• Authors: Pedeli, Xanthi; Fokianos, Konstantinos; Pourahmadi, Mohsen
• Is Part Of: Statistical modelling. Volume 15:Number 3(2015)
• Page Start: 233
• Page End: 255
• Publication Date: 2015-06
• Subjects: Cholesky decomposition -- Covariance matrix -- GARCH model -- hyperspherical coordinates -- volatility; Linear models (Statistics) -- Periodicals; Mathematical models -- Periodicals; Modèles linéaires (Statistique) -- Périodiques; Modèles mathématiques -- Périodiques; Modèle statistique; Modèle linéaire; Modélisation statistique; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 519.5011
• DOI: 10.1177/1471082X14551246
• Languages: English
• ISSNs: 1471-082X

Abstract

Parsimonious estimation of high-dimensional covariance matrices is of fundamental importance in multivariate statistics. Typical examples occur in finance, where the instantaneous dependence among several asset returns should be taken into account. Multivariate GARCH processes have been established as a standard approach for modelling such data. However, the majority of GARCH-type models are either based on strong assumptions that may not be realistic or require restrictions that are often too hard to be satisfied in practice. We consider two alternative decompositions of time-varying covariance matricesΣ t . The first is based on the modified Cholesky decomposition of the covariance matrices and second relies on the hyperspherical parametrization of the standard Cholesky factor of their correlation matricesR t . Then, we combine each Cholesky factor with the log-GARCH models for the corresponding time–varying volatilities and use a quasi maximum likelihood approach to estimate the parameters. Using log-GARCH models is quite natural for achieving the positive definiteness ofΣ t and this is a novelty of this work. Application of the proposed methodologies to two real financial datasets reveals their usefulness in terms of parsimony, ease of implementation and stresses the choice of the appropriate models using familiar data-driven processes such as various forms of the exploratory data analysis and regression.