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Methods for generating coherent distortion risk measures

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  • Samanthi, Ranadeera G.M.
  • Sepanski, Jungsywan

Abstract

This paper presents methods for generating new distortion functions utilising distribution functions and composite distribution functions. To ensure the coherency of the corresponding distortion risk measures, the concavity of the proposed distortion functions is established by restricting the parameter space of the generating distribution. Closed-form expressions for risk measures are derived for some cases. Numerical and graphical results are presented to demonstrate the effects of parameter values on the risk measures for exponential, Pareto and log-normal losses. In addition, we apply the proposed distortion functions to derive risk measures for a segregated fund guarantee.

Suggested Citation

  • Samanthi, Ranadeera G.M. & Sepanski, Jungsywan, 2019. "Methods for generating coherent distortion risk measures," Annals of Actuarial Science, Cambridge University Press, vol. 13(2), pages 400-416, September.
    • Handle: RePEc:cup:anacsi:v:13:y:2019:i:02:p:400-416_00
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    Cited by:

    1. Kim, Min Jae & Kim, Tong Seop & Flores, Robert J. & Brouwer, Jack, 2020. "Neural-network-based optimization for economic dispatch of combined heat and power systems," Applied Energy, Elsevier, vol. 265(C).
    2. Nigus Demelash Melaku & Ali Fares & Ripendra Awal, 2023. "Exploring the Impact of Winter Storm Uri on Power Outage, Air Quality, and Water Systems in Texas, USA," Sustainability, MDPI, vol. 15(5), pages 1-19, February.
    3. Soren Bettels & Sojung Kim & Stefan Weber, 2022. "Multinomial Backtesting of Distortion Risk Measures," Papers 2201.06319, arXiv.org, revised Aug 2024.

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