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Tail Conditional Expectations Based on Kumaraswamy Dispersion Models

Author

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  • Indranil Ghosh

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

  • Filipe J. Marques

    (Centro de Matemática e Aplicações (CMA), Departamento de Matemática, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Lisbon, Portugal)

Abstract

Recently, there seems to be an increasing amount of interest in the use of the tail conditional expectation (TCE) as a useful measure of risk associated with a production process, for example, in the measurement of risk associated with stock returns corresponding to the manufacturing industry, such as the production of electric bulbs, investment in housing development, and financial institutions offering loans to small-scale industries. Companies typically face three types of risk (and associated losses from each of these sources): strategic (S); operational (O); and financial (F) (insurance companies additionally face insurance risks) and they come from multiple sources. For asymmetric and bounded losses (properly adjusted as necessary) that are continuous in nature, we conjecture that risk assessment measures via univariate/bivariate Kumaraswamy distribution will be efficient in the sense that the resulting TCE based on bivariate Kumaraswamy type copulas do not depend on the marginals. In fact, almost all classical measures of tail dependence are such, but they investigate the amount of tail dependence along the main diagonal of copulas, which has often little in common with the concentration of extremes in the copula’s domain of definition. In this article, we examined the above risk measure in the case of a univariate and bivariate Kumaraswamy (KW) portfolio risk, and computed TCE based on bivariate KW type copulas. For illustrative purposes, a well-known Stock indices data set was re-analyzed by computing TCE for the bivariate KW type copulas to determine which pairs produce minimum risk in a two-component risk scenario.

Suggested Citation

  • Indranil Ghosh & Filipe J. Marques, 2021. "Tail Conditional Expectations Based on Kumaraswamy Dispersion Models," Mathematics, MDPI, vol. 9(13), pages 1-17, June.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:13:p:1478-:d:581197
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    References listed on IDEAS

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    1. Barry C. Arnold & Indranil Ghosh, 2017. "Some alternative bivariate Kumaraswamy models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(18), pages 9335-9354, September.
    2. Zinoviy Landsman & Emiliano Valdez, 2003. "Tail Conditional Expectations for Elliptical Distributions," North American Actuarial Journal, Taylor & Francis Journals, vol. 7(4), pages 55-71.
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    4. Landsman, Zinoviy & Valdez, Emiliano A., 2005. "Tail Conditional Expectations for Exponential Dispersion Models," ASTIN Bulletin, Cambridge University Press, vol. 35(1), pages 189-209, May.
    5. Indranil Ghosh, 2017. "Bivariate Kumaraswamy Models via Modified FGM Copulas: Properties and Applications," JRFM, MDPI, vol. 10(4), pages 1-13, November.
    6. Furman, Edward & Kuznetsov, Alexey & Su, Jianxi & Zitikis, Ričardas, 2016. "Tail dependence of the Gaussian copula revisited," Insurance: Mathematics and Economics, Elsevier, vol. 69(C), pages 97-103.
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