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Risk Model-at-Risk

Author

Listed:
  • Christophe Boucher

    (EconomiX - EconomiX - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique)

  • Jon Danielsson
  • Patrick Kouontchou

  • Bertrand Maillet

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 risk. A key reason for this is that risk measures are subject to model risk due, e.g., to specification and estimation uncertainty. While the authorities would like financial institutions to assess model risk, there is no accepted approach for such computations. We propose a remedy for this by a general framework for the computation of risk measures robust to model risk by empirically adjusting imperfect risk forecasts by outcomes from backtesting, 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.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Christophe Boucher & Jon Danielsson & Patrick Kouontchou & Bertrand Maillet, 2014. "Risk Model-at-Risk," Post-Print hal-01386003, HAL.
    • Handle: RePEc:hal:journl:hal-01386003
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    JEL classification:

    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • 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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