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Multiple risk factor dependence structures: Copulas and related properties

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  • Su, Jianxi
  • Furman, Edward

Abstract

Copulas have become an important tool in the modern best practice Enterprise Risk Management, often supplanting other approaches to modelling stochastic dependence. However, choosing the ‘right’ copula is not an easy task, and the temptation to prefer a tractable rather than a meaningful candidate from the encompassing copulas toolbox is strong. The ubiquitous applications of the Gaussian copula are just one illuminating example.

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  • Su, Jianxi & Furman, Edward, 2017. "Multiple risk factor dependence structures: Copulas and related properties," Insurance: Mathematics and Economics, Elsevier, vol. 74(C), pages 109-121.
    • Handle: RePEc:eee:insuma:v:74:y:2017:i:c:p:109-121
    DOI: 10.1016/j.insmatheco.2017.03.003
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    Cited by:

    1. Su, Jianxi & Hua, Lei, 2017. "A general approach to full-range tail dependence copulas," Insurance: Mathematics and Economics, Elsevier, vol. 77(C), pages 49-64.
    2. J. C. Arismendi-Zambrano & Vladimir Belitsky & Vinicius Amorim Sobreiro & Herbert Kimura, 2020. "The Implications of Tail Dependency Measures for Counterparty Credit Risk Pricing," Economics Department Working Paper Series n306-20.pdf, Department of Economics, National University of Ireland - Maynooth.
    3. Pai, Jeffrey & Ravishanker, Nalini, 2020. "Livestock mortality catastrophe insurance using fatal shock process," Insurance: Mathematics and Economics, Elsevier, vol. 90(C), pages 58-65.
    4. Yu, L. & Li, Y.P. & Huang, G.H. & Fan, Y.R. & Nie, S., 2018. "A copula-based flexible-stochastic programming method for planning regional energy system under multiple uncertainties: A case study of the urban agglomeration of Beijing and Tianjin," Applied Energy, Elsevier, vol. 210(C), pages 60-74.
    5. Furman, Edward & Kye, Yisub & Su, Jianxi, 2021. "Multiplicative background risk models: Setting a course for the idiosyncratic risk factors distributed phase-type," Insurance: Mathematics and Economics, Elsevier, vol. 96(C), pages 153-167.
    6. Yu, L. & Xiao, Y. & Jiang, S. & Li, Y.P. & Fan, Y.R. & Huang, G.H. & Lv, J. & Zuo, Q.T. & Wang, F.Q., 2020. "A copula-based fuzzy interval-random programming approach for planning water-energy nexus system under uncertainty," Energy, Elsevier, vol. 196(C).
    7. Nadezhda Gribkova & Ričardas Zitikis, 2019. "Statistical detection and classification of background risks affecting inputs and outputs," METRON, Springer;Sapienza Università di Roma, vol. 77(1), pages 1-18, April.
    8. Kiriliouk, Anna, 2020. "Hypothesis testing for tail dependence parameters on the boundary of the parameter space," Econometrics and Statistics, Elsevier, vol. 16(C), pages 121-135.

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    More about this item

    Keywords

    Multivariate distributions; (Tail) dependence; Archimedean copulas; Marshall–Olkin copulas; Factor models; Default risk;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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