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Scenario Weights for Importance Measurement (SWIM) – an R package for sensitivity analysis

Pesenti, S. M., Bettini, A., Millossovich, P. ORCID: 0000-0001-8269-7507 and Tsanakas, A. ORCID: 0000-0003-4552-5532 (2021). Scenario Weights for Importance Measurement (SWIM) – an R package for sensitivity analysis. Annals of Actuarial Science, 15(2), pp. 458-483. doi: 10.1017/S1748499521000130

Abstract

The SWIM package implements a flexible sensitivity analysis framework, based primarily on results and tools developed by Pesenti et al. (2019). SWIM provides a stressed version of a stochastic model, subject to model components (random variables) fulfilling given probabilistic constraints (stresses). Possible stresses can be applied on moments, probabilities of given events, and risk measures such as Value-at-Risk and Expected Shortfall. SWIM operates upon a single set of simulated scenarios from a stochastic model, returning scenario weights, which encode the required stress and allow monitoring the impact of the stress on all model components. The scenario weights are calculated to minimise the relative entropy with respect to the baseline model, subject to the stress applied. As well as calculating scenario weights, the package provides tools for the analysis of stressed models, including plotting facilities and evaluation of sensitivity measures. SWIM does not require additional evaluations of the simulation model or explicit knowledge of its underlying statistical and functional relations; hence it is suitable for the analysis of black box models. The capabilities of SWIM are demonstrated through a case study of a credit portfolio model.

Publication Type: Article
Additional Information: This article is published in a revised form in Annals of Actuarial Science https://doi.org/10.1017/S1748499521000130. This version is published under a Creative Commons CC-BY-NC-ND. No commercial re-distribution or re-use allowed. Derivative works cannot be distributed. © the authors, 2021.
Publisher Keywords: Sensitivity analysis; risk measures; stress testing; sensitivity measures, Kullback-Leibler divergence
Subjects: H Social Sciences > HF Commerce > HF5601 Accounting
Departments: Business School > Actuarial Science & Insurance
Date available in CRO: 29 Mar 2021 10:30
Date deposited: 29 March 2021
Date of acceptance: 26 March 2021
Date of first online publication: 12 May 2021
URI: https://openaccess.city.ac.uk/id/eprint/25845
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