A toolkit for robust risk assessment using F-divergences

Thomas Kruse, Judith C. Schneider, Nikolaus Schweizer

Research output: Contribution to journalArticleScientificpeer-review

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.
Original languageEnglish
Pages (from-to)6529-6552
JournalManagement Science
Volume67
Issue number10
Early online dateMar 2021
DOIs
Publication statusPublished - Oct 2021

Keywords

  • F-divergence
  • Model risk
  • Risk management
  • Robustness

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