A toolkit for robust risk assessment using F-divergences

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschungbegutachtet

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A toolkit for robust risk assessment using F-divergences. / Kruse, Thomas; Schneider, Judith C.; Schweizer, Nikolaus.

in: Management Science, Jahrgang 67, Nr. 10, 10.2021, S. 6529-6552.

Publikation: Beiträge in ZeitschriftenZeitschriftenaufsätzeForschungbegutachtet

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@article{166f46d436f249319c67a3c7fd1e7b40,
title = "A toolkit for robust risk assessment using F-divergences",
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 = "Management studies, Model risk, risk management, robustness, F-divergence",
author = "Thomas Kruse and Schneider, {Judith C.} and Nikolaus Schweizer",
year = "2021",
month = oct,
doi = "10.1287/mnsc.2020.3822",
language = "English",
volume = "67",
pages = "6529--6552",
journal = "Management Science",
issn = "0025-1909",
publisher = "Institute for Operations Research and the Management Sciences (I N F O R M S)",
number = "10",

}

RIS

TY - JOUR

T1 - A toolkit for robust risk assessment using F-divergences

AU - Kruse, Thomas

AU - Schneider, Judith C.

AU - Schweizer, Nikolaus

PY - 2021/10

Y1 - 2021/10

N2 - 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.

AB - 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.

KW - Management studies

KW - Model risk

KW - risk management

KW - robustness

KW - F-divergence

UR - http://www.scopus.com/inward/record.url?scp=85117249969&partnerID=8YFLogxK

U2 - 10.1287/mnsc.2020.3822

DO - 10.1287/mnsc.2020.3822

M3 - Journal articles

VL - 67

SP - 6529

EP - 6552

JO - Management Science

JF - Management Science

SN - 0025-1909

IS - 10

ER -

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