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Certainty equivalent measures of risk

Author

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  • Alexander Vinel

    (3131 Seamans Center for the Engineering Arts and Sciences)

  • Pavlo A. Krokhmal

    (3131 Seamans Center for the Engineering Arts and Sciences)

Abstract

We study a framework for constructing coherent and convex measures of risk that is inspired by infimal convolution operator, and which is shown to constitute a new general representation of these classes of risk functions. We then discuss how this scheme may be effectively applied to obtain a class of certainty equivalent measures of risk that can directly incorporate preferences of a rational decision maker as expressed by a utility function. This approach is consequently employed to introduce a new family of measures, the log-exponential convex measures of risk. Conducted numerical experiments show that this family can be a useful tool for modeling of risk-averse preferences in decision making problems with heavy-tailed distributions of uncertain parameters.

Suggested Citation

  • Alexander Vinel & Pavlo A. Krokhmal, 2017. "Certainty equivalent measures of risk," Annals of Operations Research, Springer, vol. 249(1), pages 75-95, February.
    • Handle: RePEc:spr:annopr:v:249:y:2017:i:1:d:10.1007_s10479-015-1801-0
    DOI: 10.1007/s10479-015-1801-0
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    References listed on IDEAS

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    Cited by:

    1. Haktanır, Elif & Kahraman, Cengiz, 2023. "Intuitionistic fuzzy risk adjusted discount rate and certainty equivalent methods for risky projects," International Journal of Production Economics, Elsevier, vol. 257(C).
    2. Geissel Sebastian & Sass Jörn & Seifried Frank Thomas, 2018. "Optimal expected utility risk measures," Statistics & Risk Modeling, De Gruyter, vol. 35(1-2), pages 73-87, January.
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    4. Daniela Escobar & Georg Pflug, 2018. "The distortion principle for insurance pricing: properties, identification and robustness," Papers 1809.06592, arXiv.org.
    5. Minglong Zhou & Melvyn Sim & Shao‐Wei Lam, 2022. "Advance admission scheduling via resource satisficing," Production and Operations Management, Production and Operations Management Society, vol. 31(11), pages 4002-4020, November.

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