Computing Sensitivities for Distortion Risk Measures
Peter W. Glynn, Yijie Peng, Michael C. Fu, Jian-Qiang Hu
INFORMS Journal on Computing · 2021 · Volume 33 · Issue 4
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
Enable JavaScript for citation tools and additional paper interactions.