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A Toolkit for Robust Risk Assessment Using F -Divergences

Author

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  • 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)

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.

Suggested Citation

  • Thomas Kruse & Judith C. Schneider & Nikolaus Schweizer, 2021. "A Toolkit for Robust Risk Assessment Using F -Divergences," Management Science, INFORMS, vol. 67(10), pages 6529-6552, October.
    • Handle: RePEc:inm:ormnsc:v:67:y:2021:i:10:p:6529-6552
    DOI: 10.1287/mnsc.2020.3822
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    References listed on IDEAS

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