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Measuring model risk

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  • Philipp Sibbertsen
  • Gerhard Stahl
  • Corinna Luedtke

Abstract

ABSTRACT Model risk as part of operational risk is a serious problem for financial institutions. As the pricing of derivatives as well as the computation of the market or credit risk of an institution depend on statistical models, the application of a wrong model can lead to a serious over- or underestimation of the institution's risk. Because the underlying data-generating process is unknown in practice, evaluating the model risk is a challenge. So far, definitions of model risk have been either application-oriented, including risk induced by the statistician rather than by the statistical model, or so research-oriented as to prove too abstract to be used in practice. They are especially prone not to be data driven. We introduce a data-driven notion of model risk that includes the features of the research-oriented approach, extending it by a statistical model-building procedure and thus compromising between the two definitions at hand. We further suggest the application of robust estimates to reduce the model risk, and advocate the application of stress tests with respect to the valuation of the portfolio.

Suggested Citation

  • Philipp Sibbertsen & Gerhard Stahl & Corinna Luedtke, . "Measuring model risk," Journal of Risk Model Validation, Journal of Risk Model Validation.
    • Handle: RePEc:rsk:journ5:2161282
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    References listed on IDEAS

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    1. Kerkhof, F.L.J. & Melenberg, B. & Schumacher, J.M., 2002. "Model Risk and Regulatory Capital," Discussion Paper 2002-27, Tilburg University, Center for Economic Research.
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    Cited by:

    1. Mariano González-Sánchez & Eva M. Ibáñez Jiménez & Ana I. Segovia San Juan, 2022. "Market and model risks: a feasible joint estimate methodology," Risk Management, Palgrave Macmillan, vol. 24(3), pages 187-213, September.
    2. Stahl, Gerhard & Sibbertsen, Philipp & Bertram, Philip, 2011. "Modellrisiko = Spezifikation + Validierung," Hannover Economic Papers (HEP) dp-468, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    3. Gourieroux, Christian & Tiomo, Andre, 2019. "The Evaluation of Model Risk for Probability of Default and Expected Loss," MPRA Paper 95795, University Library of Munich, Germany.
    4. Schlegel, Friederike & Hakenes, Hendrik, 2013. "Model Risk - an Agency Theoretic Approach," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79954, Verein für Socialpolitik / German Economic Association.
    5. Dannenberg, Henry, 2011. "The Importance of Estimation Uncertainty in a Multi-Rating Class Loan Portfolio," IWH Discussion Papers 11/2011, Halle Institute for Economic Research (IWH).
    6. Volker Stein & Arnd Wiedemann, 2016. "Risk governance: conceptualization, tasks, and research agenda," Journal of Business Economics, Springer, vol. 86(8), pages 813-836, November.

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    More about this item

    JEL classification:

    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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