Popular sensitivity analysis techniques such as Tornado Diagrams or the Morris method are
based on one-at-a-time input variations (local main effects, henceforth). We evidence a link …

We propose a holistic framework for constructing sensitivity measures for any elicitable
functional T of a response variable. The sensitivity measures, termed score-based …

We present a theoretical framework for stressing multivariate stochastic models. We
consider a stress to be a change of measure, placing a higher weight on multivariate …

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 …

Stress testing, and in particular, reverse stress testing, is a prominent exercise in risk
management practice. Reverse stress testing, in contrast to (forward) stress testing, aims to …

In this paper, we study the risk sharing problem among multiple agents using Lambda Value-
at-Risk as their preference functional, under heterogeneous beliefs, where beliefs are …

In risk analysis, sensitivity measures quantify the extent to which the probability distribution
of a model output is affected by changes (stresses) in individual random input factors. For …

The Scenario Weights for Importance Measurement (SWIM) package implements a flexible
sensitivity analysis framework, based primarily on results and tools developed by Pesenti et …

Given an-dimensional stochastic process driven by-Brownian motions and Poisson random
measures, we search for a probability measure, with minimal relative entropy to, such that …

We introduce an approach to sensitivity analysis of quantitative risk models, for the purpose
of identifying the most influential inputs. The proposed approach relies on a change of …