A toolkit for robust risk assessment using F-divergences
Kruse,Thomas Schneider,Judith C. Schweizer,Nikolaus
Kruse,Thomas
Schneider,Judith C.
Schweizer,Nikolaus
Abstract
This paper assembles a toolkit for the assessment of model risk when model uncertainty sets are defined in terms of an F-divergence ball around a reference model. We propose a new family of F-divergences that are easy to implement and flexible enough to imply convincing uncertainty sets for broad classes of reference models. We use our theoretical results to construct concrete examples of divergences that allow for significant amounts of uncertainty about lognormal or heavy-tailed Weibull reference models without implying that the worst case is necessarily infinitely bad. We implement our tools in an open-source software package and apply them to three risk management problems from operations management, insurance, and finance.
Description
Date
2021-10
Journal Title
Journal ISSN
Volume Title
Publisher
Research Projects
Organizational Units
Journal Issue
Keywords
F-divergence, Model risk, Risk management, Robustness
Citation
Kruse, T, Schneider, J C & Schweizer, N 2021, 'A toolkit for robust risk assessment using F-divergences', Management Science, vol. 67, no. 10, pp. 6529-6552. https://doi.org/10.1287/mnsc.2020.3822
License
info:eu-repo/semantics/openAccess