Scenario Weights for Importance Measurement (SWIM) – An R Package for Sensitivity Analysis

25 Pages Posted: 10 Feb 2020

See all articles by Silvana M. Pesenti

Silvana M. Pesenti

University of Toronto

Alberto Bettini

affiliation not provided to SSRN

Pietro Millossovich

City University London - Sir John Cass Business School

Andreas Tsanakas

City University London - Cass Business School

Date Written: January 10, 2020

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.

Keywords: Sensitivity analysis, risk measures, stress testing, sensitivity measures, Kullback-Leibler divergence

JEL Classification: C15, C52, C67, G22

Suggested Citation

Pesenti, Silvana M. and Bettini, Alberto and Millossovich, Pietro and Tsanakas, Andreas, Scenario Weights for Importance Measurement (SWIM) – An R Package for Sensitivity Analysis (January 10, 2020). Available at SSRN: https://ssrn.com/abstract=3515274 or http://dx.doi.org/10.2139/ssrn.3515274

Silvana M. Pesenti (Contact Author)

University of Toronto ( email )

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

Alberto Bettini

affiliation not provided to SSRN

Pietro Millossovich

City University London - Sir John Cass Business School ( email )

106 Bunhill Row
London, EC1Y 8TZ
United Kingdom

HOME PAGE: http://www.cass.city.ac.uk/experts/P.Millossovich

Andreas Tsanakas

City University London - Cass Business School ( email )

106 Bunhill Row
London, EC1Y 8TZ
United Kingdom

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