On a bivariate copula with both upper and lower full-range tail dependence

Lei Hua

Insurance: Mathematics and Economics, 2017, vol. 73, issue C, 94-104

Abstract: Copula functions can be useful in accounting for various dependence patterns appearing in joint tails of data. We propose a new two-parameter bivariate copula family that possesses the following features. First, both upper and lower tails are able to explain full-range tail dependence. That is, the dependence in each tail can range among quadrant tail independence, intermediate tail dependence, and usual tail dependence. Second, it can capture upper and lower tail dependence patterns that are either the same or different. We first prove the full-range tail dependence property, and then we obtain the corresponding extreme value copula. There are two applications based on the proposed copula. The first one is modeling pairwise dependence between financial markets. The second one is modeling dynamic tail dependence patterns that appear in upper and lower tails of a loss-and-expense data.

Keywords: Hypergeometric functions; Tail order; Intermediate tail dependence; Quadrant tail independence; Usual tail dependence; Beta prime scale mixtures (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:73:y:2017:i:c:p:94-104

DOI: 10.1016/j.insmatheco.2017.01.003

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