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The Pernicious Effects of Contaminated Data in Risk Management

Author

Listed:
  • Christophe Pérignon

    (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - HEC Paris - Ecole des Hautes Etudes Commerciales - CNRS - Centre National de la Recherche Scientifique)

  • Laurent Fresard

    (GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - HEC Paris - Ecole des Hautes Etudes Commerciales - CNRS - Centre National de la Recherche Scientifique)

  • Anders Wilhelmsson

    (Department of Economics - Skane University Hospital [Lund])

Abstract
Banks hold capital to guard against unexpected surges in losses and long freezes in financial markets. The minimum level of capital is set by banking regulators as a function of the banks' own estimates of their risk exposures. As a result, a great challenge for both banks and regulators is to validate internal risk models. We show that a large fraction of US and international banks uses contaminated data when testing their models. In particular, most banks validate their market risk model using profit-and-loss (P/L) data that include fees and commissions and intraday trading revenues. This practice is inconsistent with the definition of the employed market risk measure. Using both bank data and simulations, we find that data contamination has dramatic implications for model validation and can lead to the acceptance of misspecified risk models. Moreover, our estimates suggest that the use of contaminated data can significantly reduce (market-risk induced) regulatory capital.

Suggested Citation

  • Christophe Pérignon & Laurent Fresard & Anders Wilhelmsson, 2011. "The Pernicious Effects of Contaminated Data in Risk Management," Post-Print hal-00630301, HAL.
    • Handle: RePEc:hal:journl:hal-00630301
    DOI: 10.1016/j.jbankfin.2011.02.013
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    References listed on IDEAS

    as
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    Cited by:

    1. Sabiwalsky, Ralf, 2012. "Does Basel II pillar 3 risk exposure data help to identify risky banks?," SFB 649 Discussion Papers 2012-008, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    2. repec:hum:wpaper:sfb649dp2012-008 is not listed on IDEAS
    3. Boucher, Christophe M. & Daníelsson, Jón & Kouontchou, Patrick S. & Maillet, Bertrand B., 2014. "Risk models-at-risk," Journal of Banking & Finance, Elsevier, vol. 44(C), pages 72-92.
    4. Colletaz, Gilbert & Hurlin, Christophe & Pérignon, Christophe, 2013. "The Risk Map: A new tool for validating risk models," Journal of Banking & Finance, Elsevier, vol. 37(10), pages 3843-3854.
    5. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.

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