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Computing Sensitivities for Distortion Risk Measures

Author

Listed:
  • Peter W. Glynn

    (Department of Management Science and Engineering, Stanford University, Stanford, California 94305)

  • Yijie Peng

    (Department of Management Science and Information Systems, Guanghua School of Management, Peking University, Beijing 100871, China)

  • Michael C. Fu

    (The Robert H. Smith School of Business, Institute for Systems Research, University of Maryland, College Park, Maryland 20742)

  • Jian-Qiang Hu

    (Department of Management Science, School of Management, Fudan University, Shanghai 200433, China)

Abstract

Distortion risk measure, defined by an integral of a distorted tail probability, has been widely used in behavioral economics and risk management as an alternative to expected utility. The sensitivity of the distortion risk measure is a functional of certain distribution sensitivities. We propose a new sensitivity estimator for the distortion risk measure that uses generalized likelihood ratio estimators for distribution sensitivities as input and establish a central limit theorem for the new estimator. The proposed estimator can handle discontinuous sample paths and distortion functions.

Suggested Citation

  • Peter W. Glynn & Yijie Peng & Michael C. Fu & Jian-Qiang Hu, 2021. "Computing Sensitivities for Distortion Risk Measures," INFORMS Journal on Computing, INFORMS, vol. 33(4), pages 1520-1532, October.
    • Handle: RePEc:inm:orijoc:v:33:y:2021:i:4:p:1520-1532
    DOI: 10.1287/ijoc.2020.1016
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    References listed on IDEAS

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    Cited by:

    1. Yiting Fan & Rui Fang, 2022. "Some Results on Measures of Interaction among Risks," Mathematics, MDPI, vol. 10(19), pages 1-19, October.

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