A toolkit for robust risk assessment using F-divergences

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschungbegutachtet

Authors

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.

OriginalspracheEnglisch
ZeitschriftManagement Science
Jahrgang67
Ausgabenummer10
Seiten (von - bis)6529-6552
Anzahl der Seiten24
ISSN0025-1909
DOIs
PublikationsstatusErschienen - 10.2021

DOI