Computing Sensitivities for Distortion Risk Measures

Peter W. Glynn (), Yijie Peng (), Michael C. Fu () and Jian-Qiang Hu ()
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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

INFORMS Journal on Computing, 2021, vol. 33, issue 4, 1520-1532

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.

Keywords: sensitivity analysis; distortion risk measure; asymptotic analysis; functional limit theory (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:33:y:2021:i:4:p:1520-1532

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