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Risk model-at-risk

Christophe Boucher (), Jon Danielsson (), Patrick Kouontchou () and Bertrand Maillet
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Christophe Boucher: CEREFIGE - Centre Européen de Recherche en Economie Financière et Gestion des Entreprises - UL - Université de Lorraine
Patrick Kouontchou: CEREFIGE - Centre Européen de Recherche en Economie Financière et Gestion des Entreprises - UL - Université de Lorraine

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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.

Keywords: Model Risk; Value-at-risk; Backtesting (search for similar items in EconPapers)
Date: 2014
Note: View the original document on HAL open archive server: https://hal.univ-lorraine.fr/hal-01370130
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Published in Journal of Banking and Finance, Elsevier, 2014, pp.72-92

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Working Paper: Risk Model-at-Risk (2014)
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