City Research Online

Reverse sensitivity testing: What does it take to break the model?

Pesenti, S. M., Millossovich, P. ORCID: 0000-0001-8269-7507 and Tsanakas, A. ORCID: 0000-0003-4552-5532 (2018). Reverse sensitivity testing: What does it take to break the model?. European Journal of Operational Research, doi: 10.1016/j.ejor.2018.10.003

Abstract

Sensitivity analysis is an important component of model building, interpretation and validation. A model comprises a vector of random input factors, an aggregation function mapping input factors to a random output, and a (baseline) probability measure. A risk measure, such as Value-at-Risk and Expected Shortfall, maps the distribution of the output to the real line. As is common in risk management, the value of the risk measure applied to the output is a decision variable. Therefore, it is of interest to associate a critical increase in the risk measure to specific input factors. We propose a global and model-independent framework, termed `reverse sensitivity testing', comprising three steps: (a) an output stress is specified, corresponding to an increase in the risk measure(s); (b) a (stressed) probability measure is derived, minimising the Kullback-Leibler divergence with respect to the baseline probability, under constraints generated by the output stress; (c) changes in the distributions of input factors are evaluated. We argue that a substantial change in the distribution of an input factor corresponds to high sensitivity to that input and introduce a novel sensitivity measure to formalise this insight. Implementation of reverse sensitivity testing in a Monte Carlo setting can be performed on a single set of input/output scenarios, simulated under the baseline model. Thus the approach circumvents the need for additional computationally expensive evaluations of the aggregation function. We illustrate the proposed approach through numerical examples with a simple insurance portfolio and a model of a London Insurance Market portfolio used in industry.

Publication Type: Article
Additional Information: © 2018 Elsevier This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Publisher Keywords: Robustness and sensitivity analysis, risk management, Value-at-Risk, Expected Shortfall, stress testing.
Subjects: H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management
Departments: Cass Business School > Actuarial Science & Insurance
URI: http://openaccess.city.ac.uk/id/eprint/20742
[img] Text - Accepted Version
This document is not freely accessible until 10 October 2020 due to copyright restrictions.
Available under License Creative Commons Attribution Non-commercial No Derivatives.

To request a copy, please use the button below.

Request a copy
Supplementary Materials:

Export

Downloads

Downloads per month over past year

View more statistics

Actions (login required)

Admin Login Admin Login