Journal article
t-copula from the viewpoint of tail dependence matrices
Journal of multivariate analysis, Vol.191, 105027
09/01/2022
DOI: 10.1016/j.jmva.2022.105027
Abstract
The tail dependence coefficient is a bivariate measure of dependence in the tail, and the Tail Dependence Matrix (TDM) is a bidimensional array of these coefficients corresponding to a random vector. The TDM serves as a parsimonious measure of multivariate tail dependence akin to the correlation matrix in the context of dependence. The set of all TDMs corresponding to d-dimensional random vectors is a convex polytope with an intricate description known only for d up to six, with both its numbers of facets and vertices growing at least exponentially in d. We posit that the richness of its subset that a copula family can accommodate is a practically vital feature to be considered in its choice for modeling in the presence of tail dependence. In this paper, our focus is on the t-copula family, which is a popular choice for parametric modeling in risk management and financial econometrics. We discuss some geometric properties of the subset of TDMs supported by the t-copula family and provide an efficient algorithm to determine the t-copula that best captures the tail dependence specified by a target TDM.
Details
- Title: Subtitle
- t-copula from the viewpoint of tail dependence matrices
- Creators
- Nariankadu Shyamalkumar - University of IowaSiyang Tao - Ball State University
- Resource Type
- Journal article
- Publication Details
- Journal of multivariate analysis, Vol.191, 105027
- Publisher
- Elsevier BV
- DOI
- 10.1016/j.jmva.2022.105027
- ISSN
- 0047-259X
- eISSN
- 1095-7243
- Language
- English
- Date published
- 09/01/2022
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984530387902771
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