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Asymptotic properties of generalized shortfall risk measures for heavy-tailed risks

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  • Mao, Tiantian
  • Stupfler, Gilles
  • Yang, Fan

Abstract

We study a general risk measure called the generalized shortfall risk measure, which was first introduced in Mao and Cai (2018). It is proposed under the rank-dependent expected utility framework, or equivalently induced from the cumulative prospect theory. This risk measure can be flexibly designed to capture the decision maker's behavior toward risks and wealth when measuring risk. In this paper, we derive the first- and second-order asymptotic expansions for the generalized shortfall risk measure. Our asymptotic results can be viewed as unifying theory for, among others, distortion risk measures and utility-based shortfall risk measures. They also provide a blueprint for the estimation of these measures at extreme levels, and we illustrate this principle by constructing and studying a quantile-based estimator in a special case. The accuracy of the asymptotic expansions and of the estimator is assessed on several numerical examples.

Suggested Citation

  • Mao, Tiantian & Stupfler, Gilles & Yang, Fan, 2023. "Asymptotic properties of generalized shortfall risk measures for heavy-tailed risks," Insurance: Mathematics and Economics, Elsevier, vol. 111(C), pages 173-192.
    • Handle: RePEc:eee:insuma:v:111:y:2023:i:c:p:173-192
    DOI: 10.1016/j.insmatheco.2023.05.001
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    References listed on IDEAS

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    1. Ulrich Schmidt & Horst Zank, 2007. "Linear cumulative prospect theory with applications to portfolio selection and insurance demand," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 30(1), pages 1-18, May.
    2. Hua, Lei & Joe, Harry, 2011. "Second order regular variation and conditional tail expectation of multiple risks," Insurance: Mathematics and Economics, Elsevier, vol. 49(3), pages 537-546.
    3. Cai, J. & Einmahl, J.H.J. & de Haan, L.F.M. & Zhou, C., 2012. "Estimation of the Marginal Expected Shortfall : The Mean when a Related Variable is Extreme," Other publications TiSEM e96e039f-cb6b-4cd5-805b-5, Tilburg University, School of Economics and Management.
    4. Loretan, Mico & Phillips, Peter C. B., 1994. "Testing the covariance stationarity of heavy-tailed time series: An overview of the theory with applications to several financial datasets," Journal of Empirical Finance, Elsevier, vol. 1(2), pages 211-248, January.
    5. Juan-Juan Cai & John H. J. Einmahl & Laurens Haan & Chen Zhou, 2015. "Estimation of the marginal expected shortfall: the mean when a related variable is extreme," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(2), pages 417-442, March.
    6. Tiantian Mao & Jun Cai, 2018. "Risk measures based on behavioural economics theory," Finance and Stochastics, Springer, vol. 22(2), pages 367-393, April.
    7. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    8. Kaluszka, Marek & Krzeszowiec, Michał, 2012. "Mean-Value Principle under Cumulative Prospect Theory," ASTIN Bulletin, Cambridge University Press, vol. 42(1), pages 103-122, May.
    9. Xavier Gabaix & Parameswaran Gopikrishnan & Vasiliki Plerou & H. Eugene Stanley, 2003. "A theory of power-law distributions in financial market fluctuations," Nature, Nature, vol. 423(6937), pages 267-270, May.
    10. Mao, Tiantian & Yang, Fan, 2015. "Risk concentration based on Expectiles for extreme risks under FGM copula," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 429-439.
    11. Kaluszka, Marek & Krzeszowiec, Michał, 2012. "Pricing insurance contracts under Cumulative Prospect Theory," Insurance: Mathematics and Economics, Elsevier, vol. 50(1), pages 159-166.
    12. Cai, Jun & Mao, Tiantian, 2020. "Risk Measures Derived From A Regulator’S Perspective On The Regulatory Capital Requirements For Insurers," ASTIN Bulletin, Cambridge University Press, vol. 50(3), pages 1065-1092, September.
    13. Xavier Gabaix, 2009. "Power Laws in Economics and Finance," Annual Review of Economics, Annual Reviews, vol. 1(1), pages 255-294, May.
    14. Abdelaati Daouia & Stéphane Girard & Gilles Stupfler, 2018. "Estimation of tail risk based on extreme expectiles," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(2), pages 263-292, March.
    15. Newey, Whitney K & Powell, James L, 1987. "Asymmetric Least Squares Estimation and Testing," Econometrica, Econometric Society, vol. 55(4), pages 819-847, July.
    16. Yanchun Zhao & Tiantian Mao & Fan Yang, 2021. "Estimation of the Haezendonck-Goovaerts risk measure for extreme risks," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2021(7), pages 599-622, August.
    17. Tang, Qihe & Yang, Fan, 2012. "On the Haezendonck–Goovaerts risk measure for extreme risks," Insurance: Mathematics and Economics, Elsevier, vol. 50(1), pages 217-227.
    18. Mao, Tiantian & Hu, Taizhong, 2012. "Second-order properties of the Haezendonck–Goovaerts risk measure for extreme risks," Insurance: Mathematics and Economics, Elsevier, vol. 51(2), pages 333-343.
    19. Bellini, Fabio & Klar, Bernhard & Müller, Alfred & Rosazza Gianin, Emanuela, 2014. "Generalized quantiles as risk measures," Insurance: Mathematics and Economics, Elsevier, vol. 54(C), pages 41-48.
    20. Hua, Lei & Joe, Harry, 2014. "Strength of tail dependence based on conditional tail expectation," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 143-159.
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    More about this item

    Keywords

    Generalized shortfall risk measure; Asymptotic expansions; Heavy tails; Estimation; Extreme value statistics;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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