A Toolkit for Robust Risk Assessment Using F -Divergences

Thomas Kruse (), Judith C. Schneider () and Nikolaus Schweizer ()
Additional contact information
Thomas Kruse: Institute of Mathematics, Justus Liebig University, 35392 Giessen, Germany
Judith C. Schneider: Chair of Finance, Leuphana University, 21335 Lüneburg, Germany; Finance Center Münster, University of Münster, 48143 Münster, Germany
Nikolaus Schweizer: Department of Econometrics and Operations Research, Tilburg University, 5037AB Tilburg, Netherlands

Management Science, 2021, vol. 67, issue 10, 6529-6552

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.

Keywords: F -divergence; model risk; risk management; robustness (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.2020.3822 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:67:y:2021:i:10:p:6529-6552

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().