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

Published online by Cambridge University Press:  12 May 2021

Silvana M. Pesenti Open the ORCID record for Silvana M. Pesenti [Opens in a new window] ,
Alberto Bettini ,
Pietro Millossovich  and
Andreas Tsanakas Open the ORCID record for Andreas Tsanakas [Opens in a new window]
Silvana M. Pesenti*
Affiliation:
Department of Statistical Sciences, University of Toronto, Toronto, M5G 1X6, Canada
Alberto Bettini
Affiliation:
Assicurazioni Generali S.p.A, Trieste, 34132, Italy
Pietro Millossovich
Affiliation:
DEAMS, University of Trieste, Trieste, 34127, Italy Faculty of Actuarial Science and Insurance, The Business School (formerly Cass), City, University of London, London, EC1Y 8TZ, United Kingdom
Andreas Tsanakas
Affiliation:
Faculty of Actuarial Science and Insurance, The Business School (formerly Cass), City, University of London, London, EC1Y 8TZ, United Kingdom
*
*Corresponding author. E-mail: [email protected]
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Abstract

The Scenario Weights for Importance Measurement (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.

Keywords

Sensitivity analysisRisk measuresStress testingSensitivity measuresKullback–Leibler divergence
Type
Paper
Information
Annals of Actuarial Science , Volume 15 , Issue 2 , July 2021 , pp. 458 - 483
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of Institute and Faculty of Actuaries

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