Silvana M. Pesenti, Cass University - Andreas Tsanakas Faculty of Actuarial Science and Insurance, Cass Business School, City, University of London - Pietro Millossovich DEAMS, University of Trieste
Date: 12 APRIL 2018 at 14:30
Event location: Aula II - Dipartimento di Scienze Statistiche, piano terra
Type: Statistics Seminars
Abstract:
Sensitivity analysis is an important component of model building, interpretation and validation. A model comprises a vector of random input factors, an aggregation function mapping input factors to a random output, and a (baseline) probability measure. A risk measure, such as Value-at-Risk and Expected Shortfall, maps the distribution of the output to the real line. As is common in risk management, the value of the risk measure applied to the output is a decision variable. Therefore, it is of interest to associate a critical increase in the risk measure to specific input factors. We propose a global and model-independent framework, termed `reverse sensitivity testing', comprising three steps: (a) an output stress is specified, corresponding to an increase in the risk measure(s); (b) a (stressed) probability measure is derived, minimising the Kullback-Leibler divergence with respect to the baseline probability, under constraints generated by the output stress; (c) changes in the distributions of input factors are evaluated. We argue that a substantial change in the distribution of an input factor corresponds to high sensitivity to that input and introduce a novel sensitivity measure to formalise this insight. Implementation of reverse sensitivity testing in a Monte-Carlo setting can be performed on a single set of input/output scenarios, simulated under the baseline model. Thus the approach circumvents the need for additional computationally expensive evaluations of the aggregation function. We illustrate the proposed approach through a numerical example of a simple insurance portfolio and a model of a London Insurance Market portfolio used in industry.
L’Organizzatore Il Direttore
Prof. Sabrina Mulinacci Prof. Angela Montanari
La S.V. è invitata