Reverse Sensitivity Analysis for Risk Modelling

35 Pages Posted: 14 Jul 2021

Date Written: July 1, 2021

Abstract

We consider the problem where a modeller conducts sensitivity analysis of a model consisting of random input factors, a corresponding random output of interest, and a baseline probability measure. The modeller seeks to understand how the model (the distribution of the input factors as well as the output) changes under a stress on the output's distribution. Specifically, for a stress on the output random variable, we derive the unique stressed distribution of the output that is closest in the Wasserstein distance to the baseline output's distribution and satisfies the stress. We further derive the stressed model, including the stressed distribution of the inputs, which can be calculated in a numerically efficient way from a set of baseline Monte Carlo samples.

The proposed reverse sensitivity analysis framework is model-free and allows for stresses on the output such as (a) the mean and variance, (b) any distortion risk measure including the Value-at-Risk and Expected-Shortfall, and (c) expected utility type constraints, thus making the reverse sensitivity analysis framework suitable for risk models.

Keywords: Distortion Risk Measures, Expected Utility, Wasserstein Distance, Robustness and Sensitivity Analysis, Model Uncertainty

Suggested Citation

Pesenti, Silvana M., Reverse Sensitivity Analysis for Risk Modelling (July 1, 2021). Available at SSRN: https://ssrn.com/abstract=3878879 or http://dx.doi.org/10.2139/ssrn.3878879

Silvana M. Pesenti (Contact Author)

University of Toronto ( email )

100 St. George Street
Toronto, Ontario M5S 3G8
Canada

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