A general approach to full-range tail dependence copulas

Jianxi Su and Lei Hua

Insurance: Mathematics and Economics, 2017, vol. 77, issue C, 49-64

Abstract: Full-range tail dependence copulas have recently been proved very useful for modeling various dependence patterns in the joint distributional tails. However, there are only a few applicable candidate models that have the full-range tail dependence property. In this paper, we present a general approach to constructing bivariate copulas that have full-range tail dependence in both upper and lower tails and are able to account for both reflection symmetry and reflection asymmetry. The general approach is based on mixtures of positive regularly varying random variables, and the full-range tail dependence property is established for such a general model. In order to construct copulas that possess the above dependence properties and are fast to compute, we construct a full-range tail dependence copula based on mixtures of Pareto random variables. We derive dependence properties of the proposed copula, and the extreme value copula based on it. A comparison with the full-range tail dependence copula proposed in Hua (2017) has been conducted, and the computational speed has been largely improved by the copula proposed in the current paper.

Keywords: Scale mixtures; Regularly varying; Pareto distribution; Fast computational speed; Asymptotic dependence; Asymptotic independence (search for similar items in EconPapers)
JEL-codes: C02 C51 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:77:y:2017:i:c:p:49-64

DOI: 10.1016/j.insmatheco.2017.08.009

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