IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v67y2021i10p6529-6552.html
   My bibliography  Save this article

A Toolkit for Robust Risk Assessment Using F -Divergences

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.2020.3822
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mnsc.2020.3822?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Dimitris Bertsimas & Melvyn Sim, 2004. "The Price of Robustness," Operations Research, INFORMS, vol. 52(1), pages 35-53, February.
    2. Henry Lam, 2016. "Robust Sensitivity Analysis for Stochastic Systems," Mathematics of Operations Research, INFORMS, vol. 41(4), pages 1248-1275, November.
    3. Kostas Bimpikis & Mihalis G. Markakis, 2016. "Inventory Pooling Under Heavy-Tailed Demand," Management Science, INFORMS, vol. 62(6), pages 1800-1813, June.
    4. Aharon Ben-Tal & Dick den Hertog & Anja De Waegenaere & Bertrand Melenberg & Gijs Rennen, 2013. "Robust Solutions of Optimization Problems Affected by Uncertain Probabilities," Management Science, INFORMS, vol. 59(2), pages 341-357, April.
    5. Paul Glasserman & Xingbo Xu, 2014. "Robust risk measurement and model risk," Quantitative Finance, Taylor & Francis Journals, vol. 14(1), pages 29-58, January.
    6. Yuhong Xu, 2014. "Robust valuation and risk measurement under model uncertainty," Papers 1407.8024, arXiv.org.
    7. Schneider, Judith C. & Schweizer, Nikolaus, 2015. "Robust measurement of (heavy-tailed) risks: Theory and implementation," Journal of Economic Dynamics and Control, Elsevier, vol. 61(C), pages 183-203.
    8. A. Ahmadi-Javid, 2012. "Entropic Value-at-Risk: A New Coherent Risk Measure," Journal of Optimization Theory and Applications, Springer, vol. 155(3), pages 1105-1123, December.
    9. Thomas Breuer & Imre Csiszár, 2016. "Measuring Distribution Model Risk," Mathematical Finance, Wiley Blackwell, vol. 26(2), pages 395-411, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Thomas Kruse & Judith C. Schneider & Nikolaus Schweizer, 2019. "Technical Note—The Joint Impact of F -Divergences and Reference Models on the Contents of Uncertainty Sets," Operations Research, INFORMS, vol. 67(2), pages 428-435, March.
    2. Jose Blanchet & Karthyek Murthy, 2019. "Quantifying Distributional Model Risk via Optimal Transport," Mathematics of Operations Research, INFORMS, vol. 44(2), pages 565-600, May.
    3. Penev, Spiridon & Shevchenko, Pavel V. & Wu, Wei, 2019. "The impact of model risk on dynamic portfolio selection under multi-period mean-standard-deviation criterion," European Journal of Operational Research, Elsevier, vol. 273(2), pages 772-784.
    4. Spiridon Penev & Pavel V. Shevchenko & Wei Wu, 2021. "The impact of model risk on dynamic portfolio selection under multi-period mean-standard-deviation criterion," Papers 2108.02633, arXiv.org.
    5. Thomas Kruse & Judith C. Schneider & Nikolaus Schweizer, 2015. "What's in a ball? Constructing and characterizing uncertainty sets," Papers 1510.01675, arXiv.org.
    6. Yu Feng & Ralph Rudd & Christopher Baker & Qaphela Mashalaba & Melusi Mavuso & Erik Schlögl, 2021. "Quantifying the Model Risk Inherent in the Calibration and Recalibration of Option Pricing Models," Risks, MDPI, vol. 9(1), pages 1-20, January.
    7. Sebastian Jaimungal & Silvana M. Pesenti & Leandro S'anchez-Betancourt, 2022. "Minimal Kullback-Leibler Divergence for Constrained L\'evy-It\^o Processes," Papers 2206.14844, arXiv.org, revised Aug 2022.
    8. Cohen, Asaf & Saha, Subhamay, 2021. "Asymptotic optimality of the generalized cμ rule under model uncertainty," Stochastic Processes and their Applications, Elsevier, vol. 136(C), pages 206-236.
    9. Yu Feng, 2019. "Theory and Application of Model Risk Quantification," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 3-2019.
    10. Henry Lam, 2018. "Sensitivity to Serial Dependency of Input Processes: A Robust Approach," Management Science, INFORMS, vol. 64(3), pages 1311-1327, March.
    11. Aleksandrina Goeva & Henry Lam & Huajie Qian & Bo Zhang, 2019. "Optimization-Based Calibration of Simulation Input Models," Operations Research, INFORMS, vol. 67(5), pages 1362-1382, September.
    12. Zhaolin Hu & L. Jeff Hong, 2022. "Robust Simulation with Likelihood-Ratio Constrained Input Uncertainty," INFORMS Journal on Computing, INFORMS, vol. 34(4), pages 2350-2367, July.
    13. Li, Jing, 2018. "Essays on model uncertainty in financial models," Other publications TiSEM 202cd910-7ef1-4db4-94ae-d, Tilburg University, School of Economics and Management.
    14. Jun-ya Gotoh & Michael Jong Kim & Andrew E. B. Lim, 2020. "Worst-case sensitivity," Papers 2010.10794, arXiv.org.
    15. Pesenti, Silvana M. & Millossovich, Pietro & Tsanakas, Andreas, 2019. "Reverse sensitivity testing: What does it take to break the model?," European Journal of Operational Research, Elsevier, vol. 274(2), pages 654-670.
    16. Kim, Sojung & Weber, Stefan, 2022. "Simulation methods for robust risk assessment and the distorted mix approach," European Journal of Operational Research, Elsevier, vol. 298(1), pages 380-398.
    17. Roberto Baviera & Giulia Bianchi, 2019. "Model risk in mean-variance portfolio selection: an analytic solution to the worst-case approach," Papers 1902.06623, arXiv.org, revised Dec 2019.
    18. Yu Feng & Erik Schlogl, 2018. "Model Risk Measurement Under Wasserstein Distance," Research Paper Series 393, Quantitative Finance Research Centre, University of Technology, Sydney.
    19. Mohammed Berkhouch & Fernanda Maria Müller & Ghizlane Lakhnati & Marcelo Brutti Righi, 2022. "Deviation-Based Model Risk Measures," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 527-547, February.
    20. Roberto Baviera & Giulia Bianchi, 2021. "Model risk in mean-variance portfolio selection: an analytic solution to the worst-case approach," Journal of Global Optimization, Springer, vol. 81(2), pages 469-491, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormnsc:v:67:y:2021:i:10:p:6529-6552. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.