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La Value-at-Risk: Modèles de la VaR, simulations en Visual Basic (Excel) et autres mesures récentes du risque de marché

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Author Info

  • Francois-Éric Racicot

    ()
    (Département des sciences administratives, Université du Québec (Outaouais) et LRSP)

  • Raymond Théoret

    ()
    (Département de stratégie des affaires, Université du Québec (Montréal))

Abstract

Since the end of the nineties, Basle Committee has required that banks compute periodically their VaR and maintain sufficient capital to pay the eventual losses projected by VaR. Unfortunately, there is not only one measure of VaR because volatility, which is a fundamental component of VaR, is latent. Therefore, banks must use many VaR models to compute the range of their prospective losses. These computations might be complex because the distribution of high frequency returns is not normal. This article analyses many VaR models and produces their programs in Visual Basic. It considers also other new measures of market risk and the use of copulas and Fourier Transform for the computation of VaR.

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File URL: http://www.repad.org/ca/qc/uq/uqo/dsa/VaRRacicotTheoret.pdf
File Function: First version, 2006
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Bibliographic Info

Paper provided by Département des sciences administratives, UQO in its series RePAd Working Paper Series with number UQO-DSA-wp022006.

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Length: 77 pages
Date of creation: 12 Jan 2006
Date of revision:
Handle: RePEc:pqs:wpaper:022006

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Keywords: Ingénierie financière; simulation de Monte Carlo; banques; copules; transformée de Fourier.;

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  1. GIOT, Pierre & LAURENT, Sébastien, 2001. "Value-at-risk for long and short trading positions," CORE Discussion Papers 2001022, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  2. Fama, Eugene F & French, Kenneth R, 1992. " The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-65, June.
  3. Cooley, Philip L, 1977. "A Multidimensional Analysis of Institutional Investor Perception of Risk," Journal of Finance, American Finance Association, vol. 32(1), pages 67-78, March.
  4. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
  5. Simon Benninga, 2000. "Financial Modeling, 2nd Edition," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262024829.
  6. Fung, William & Hsieh, David A, 1997. "Empirical Characteristics of Dynamic Trading Strategies: The Case of Hedge Funds," Review of Financial Studies, Society for Financial Studies, vol. 10(2), pages 275-302.
  7. Kraus, Alan & Litzenberger, Robert H, 1976. "Skewness Preference and the Valuation of Risk Assets," Journal of Finance, American Finance Association, vol. 31(4), pages 1085-1100, September.
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