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Bivariate Kumaraswamy Models via Modified FGM Copulas: Properties and Applications

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  • Indranil Ghosh

    (Department of Mathematics and Statistics, University of North Carolina, Wilmington, NC 28403, USA)

Abstract

A copula is a useful tool for constructing bivariate and/or multivariate distributions. In this article, we consider a new modified class of FGM (Farlie–Gumbel–Morgenstern) bivariate copula for constructing several different 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, ρ and Kendall’s τ .

Suggested Citation

  • Indranil Ghosh, 2017. "Bivariate Kumaraswamy Models via Modified FGM Copulas: Properties and Applications," JRFM, MDPI, vol. 10(4), pages 1-13, November.
    • Handle: RePEc:gam:jjrfmx:v:10:y:2017:i:4:p:19-:d:117240
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    References listed on IDEAS

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    1. I. Bairamov & S. Kotz & M. Bekci, 2001. "New generalized Farlie-Gumbel-Morgenstern distributions and concomitants of order statistics," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(5), pages 521-536.
    2. Rodríguez-Lallena, José Antonio & Úbeda-Flores, Manuel, 2004. "A new class of bivariate copulas," Statistics & Probability Letters, Elsevier, vol. 66(3), pages 315-325, February.
    3. Christian Genest & Michel Gendron & Michaël Bourdeau-Brien, 2009. "The Advent of Copulas in Finance," The European Journal of Finance, Taylor & Francis Journals, vol. 15(7-8), pages 609-618.
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    Cited by:

    1. Indranil Ghosh & Filipe J. Marques, 2021. "Tail Conditional Expectations Based on Kumaraswamy Dispersion Models," Mathematics, MDPI, vol. 9(13), pages 1-17, June.
    2. Stephen Chan & Saralees Nadarajah, 2020. "Extreme Values and Financial Risk," JRFM, MDPI, vol. 13(2), pages 1-3, February.

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