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Reverse Sensitivity Analysis for Risk Modelling
- Source :
- Risks; Volume 10; Issue 7; Pages: 141
- Publication Year :
- 2022
- Publisher :
- MDPI AG, 2022.
-
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 and which is implemented in the R package SWIM on CRAN. 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.
- Subjects :
- History
Polymers and Plastics
Strategy and Management
Monte Carlo method
Economics, Econometrics and Finance (miscellaneous)
Variance (accounting)
Industrial and Manufacturing Engineering
FOS: Economics and business
Distribution (mathematics)
Risk Management (q-fin.RM)
Accounting
Distortion risk measure
Applied mathematics
Sensitivity (control systems)
Business and International Management
Random variable
Expected utility hypothesis
Mathematics
Probability measure
distortion risk measures
expected utility
Wasserstein distance
robustness and sensitivity analysis
model uncertainty
Quantitative Finance - Risk Management
Subjects
Details
- ISSN :
- 22279091
- Volume :
- 10
- Database :
- OpenAIRE
- Journal :
- Risks
- Accession number :
- edsair.doi.dedup.....e11409a659f9dd298fe2a25e7bd55e9d
- Full Text :
- https://doi.org/10.3390/risks10070141