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Une évaluation économique du risque de modèle pour les investisseurs de long-terme

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Listed:
  • Christophe Boucher

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, A.A.Advisors-QCG - ABN AMRO)

  • Benjamin Hamidi

    (Neuflize OBC Investissements - Neuflize OBC Investissements)

  • Patrick Kouontchou

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

  • Bertrand Maillet

    (A.A.Advisors-QCG - ABN AMRO, LEO - Laboratoire d'Économie d'Orleans [UMR7322] - UO - Université d'Orléans - UT - Université de Tours - CNRS - Centre National de la Recherche Scientifique)

Abstract

The recent experience from the global financial crisis has raised serious questions about the accuracy of standard risk measures as a tool to quantify extreme downward risks. These standard risk measures, such as the VaR, emerge over the last decades as the industry standard for risk management and asset allocation (Basak and Shapiro [2001]; Montfort [2008]). We estimate the riskiness of risk models and we evaluate its impact on optimal portfolios at various time horizons. Based on a long sample of U.S. data, we find an inverse U-shape relation between VaR model errors and the horizon that impacts the optimal asset allocation of the representative agent.

Suggested Citation

  • Christophe Boucher & Benjamin Hamidi & Patrick Kouontchou & Bertrand Maillet, 2012. "Une évaluation économique du risque de modèle pour les investisseurs de long-terme," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00825337, HAL.
  • Handle: RePEc:hal:cesptp:halshs-00825337
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00825337
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    References listed on IDEAS

    as
    1. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-862, November.
    2. Basak, Suleyman & Shapiro, Alexander, 2001. "Value-at-Risk-Based Risk Management: Optimal Policies and Asset Prices," The Review of Financial Studies, Society for Financial Studies, vol. 14(2), pages 371-405.
    3. M.J.B. Hall, 1996. "The amendment to the capital accord to incorporate market risk," BNL Quarterly Review, Banca Nazionale del Lavoro, vol. 49(197), pages 271-277.
    4. Engle, Robert F. & Manganelli, Simone, 2001. "Value at risk models in finance," Working Paper Series 75, European Central Bank.
    5. Patrick Gagliardini & Christian Gouriéroux & Alain Monfort, 2010. "Microinformation, Nonlinear Filtering, and Granularity," Journal of Financial Econometrics, Oxford University Press, vol. 10(1), pages 1-53, 2012 10 1.
    6. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    7. T. Clifton Green & Stephen Figlewski, 1999. "Market Risk and Model Risk for a Financial Institution Writing Options," Journal of Finance, American Finance Association, vol. 54(4), pages 1465-1499, August.
    8. Kerkhof, Jeroen & Melenberg, Bertrand & Schumacher, Hans, 2010. "Model risk and capital reserves," Journal of Banking & Finance, Elsevier, vol. 34(1), pages 267-279, January.
    9. Rama Cont, 2006. "Model Uncertainty And Its Impact On The Pricing Of Derivative Instruments," Mathematical Finance, Wiley Blackwell, vol. 16(3), pages 519-547, July.
    10. Nicholas Barberis, 2000. "Investing for the Long Run when Returns Are Predictable," Journal of Finance, American Finance Association, vol. 55(1), pages 225-264, February.
    11. Rama Cont, 2006. "Model uncertainty and its impact on the pricing of derivative instruments," Post-Print halshs-00002695, HAL.
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    More about this item

    Keywords

    financial crisis; asset allocation.; allocation d'actifs; turbulence financière; VaR; allocation d'actifs.;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • 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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