Preprint Article Version 1 This version is not peer-reviewed

Bivariate Kumaraswamy Models via Modified Symmetric FGM Copulas: Properties and Applications in Insurance Modeling

Version 1 : Received: 23 September 2017 / Approved: 25 September 2017 / Online: 25 September 2017 (06:55:52 CEST)

A peer-reviewed article of this Preprint also exists.

Ghosh, I. Bivariate Kumaraswamy Models via Modified FGM Copulas: Properties and Applications. J. Risk Financial Manag. 2017, 10, 19. Ghosh, I. Bivariate Kumaraswamy Models via Modified FGM Copulas: Properties and Applications. J. Risk Financial Manag. 2017, 10, 19.

Journal reference: J. Risk Financial Manag. 2017, 10, 19
DOI: 10.3390/jrfm10040019

Abstract

A copula is a useful tool for constructing bivariate and/or multivariate distributions. In this article, we consider a new modi fied class of (Farlie-Gumbel-Morgenstern) FGM bivariate copula for constructing several di erent bivariate Kumaraswamy type copulas and discuss their structural properties, including dependence structures. It is established that construction of bivariate distributions by this method allows for greater flexibility in the values of Spearman's correlation coefficient rho,  and Kendall's tau . For illustrative purposes, one representative data set is utilized to exhibit the applicability of these proposed bivariate copula models.

Subject Areas

Bivariate Kumaraswamy distribution; copula based construction; Kendall'stau; dependence structures; application in insurance risk modeling

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