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Risk models-at-risk

Author

Listed:
  • Christophe Boucher

    (A.A.Advisors-QCG - ABN AMRO, CEREFIGE - Centre Européen de Recherche en Economie Financière et Gestion des Entreprises - UL - Université de Lorraine)

  • Jón Daníelsson

    (LSE - London School of Economics and Political Science)

  • Patrick Kouontchou

    (CEREFIGE - Centre Européen de Recherche en Economie Financière et Gestion des Entreprises - UL - Université de Lorraine)

  • Bertrand Maillet

    (LEO - Laboratoire d'Économie d'Orleans - CNRS - Centre National de la Recherche Scientifique - Université de Tours - UO - Université d'Orléans, CEMOI - Centre d'Économie et de Management de l'Océan Indien - UR - Université de La Réunion, A.A.Advisors-QCG - ABN AMRO)

Abstract

The experience from the global financial crisis has raised serious concerns about the accuracy of standard risk measures as tools for the quantification of extreme downward risks. A key reason for this is that risk measures are subject to a model risk due, e.g. to specification and estimation uncertainty. While regulators have proposed that financial institutions assess the model risk, there is no accepted approach for computing such a risk. We propose a remedy for this by a general framework for the computation of risk measures robust to model risk by empirically adjusting the imperfect risk forecasts by outcomes from backtesting frameworks, considering the desirable quality of VaR models such as the frequency, independence and magnitude of violations. We also provide a fair comparison between the main risk models using the same metric that corresponds to model risk required corrections.

Suggested Citation

  • Christophe Boucher & Jón Daníelsson & Patrick Kouontchou & Bertrand Maillet, 2014. "Risk models-at-risk," Post-Print hal-01243413, HAL.
  • Handle: RePEc:hal:journl:hal-01243413
    DOI: 10.1016/j.jbankfin.2014.03.019
    Note: View the original document on HAL open archive server: https://hal.univ-reunion.fr/hal-01243413
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    15. 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, August.
    16. Zuzana Krajcovicova & Pedro Pablo Perez-Velasco & Carlos Vazquez, 2017. "A Novel Approach to Quantification of Model Risk for Practitioners," Papers 1705.05572, arXiv.org.
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    21. Yesol Huh, 2014. "Machines vs. Machines: High Frequency Trading and Hard Information," Finance and Economics Discussion Series 2014-33, Board of Governors of the Federal Reserve System (U.S.).
    22. Pelster, Matthias & Vilsmeier, Johannes, 2016. "The determinants of CDS spreads: Evidence from the model space," Discussion Papers 43/2016, Deutsche Bundesbank.
    23. Kamila Sommer, 2014. "Fertility Choice in a Life Cycle Model with Idiosyncratic Uninsurable Earnings Risk," Finance and Economics Discussion Series 2014-32, Board of Governors of the Federal Reserve System (U.S.).
    24. Bayer, Sebastian, 2018. "Combining Value-at-Risk forecasts using penalized quantile regressions," Econometrics and Statistics, Elsevier, vol. 8(C), pages 56-77.
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    More about this item

    Keywords

    Backtesting; Model Risk; Revue AERES; Value-at-risk;
    All these keywords.

    JEL classification:

    • F3 - International Economics - - International Finance
    • G3 - Financial Economics - - Corporate Finance and Governance

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