A Toolkit for Robust Risk Assessment Using F-Divergences

• 成果类型: Article
• 署名作者: Kruse, Thomas; Schneider, Judith C.; Schweizer, Nikolaus
• 署名单位: Justus Liebig University Giessen; Leuphana University Luneburg; University of Munster; Tilburg University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2020.3822
• 发表日期: 2021
• 页码: 6529-6552
• 关键词: F-divergence; Model risk; risk management; Robustness

摘要

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.