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The Stress VaR: A New Risk Concept for Extreme Risk and Fund Allocation

Author

Listed:
  • Cyril Coste

    (ENS Cachan - École normale supérieure - Cachan)

  • Raphaël Douady

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

  • Ilija I. Zovko

    (CeNDEF - Center for Nonlinear Dynamics in Economics and Finance - UvA - University of Amsterdam [Amsterdam] = Universiteit van Amsterdam)

Abstract

In this article the authors introduce an approach to risk estimation based on nonlinear factor-models--the "StressVaR" (SVaR). Developed to evaluate the risk of hedge funds, the SVaR appears to be applicable to a wide range of investments. The computation of the StressVaR is a three-step procedure whose main component is to use the fairly short and sparse history of the hedge fund returns to identify relevant risk factors among a very broad set of possible risk sources. This risk profile is obtained by calibrating a polymodel, which is a collection of nonlinear single-factor models, as opposed to a single multi-factor model. The authors then use the risk profile and the very long and rich history of the factors to assess the possible impact of known past crises on the funds, unveiling their hidden risks and so called "black swans."

Suggested Citation

  • Cyril Coste & Raphaël Douady & Ilija I. Zovko, 2011. "The Stress VaR: A New Risk Concept for Extreme Risk and Fund Allocation," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00666234, HAL.
    • Handle: RePEc:hal:cesptp:hal-00666234
    DOI: 10.3905/jai.2011.13.3.010
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    References listed on IDEAS

    as
    1. Giot, Pierre & Laurent, Sebastien, 2004. "Modelling daily Value-at-Risk using realized volatility and ARCH type models," Journal of Empirical Finance, Elsevier, vol. 11(3), pages 379-398, June.
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    3. Hang Chan, Ngai & Deng, Shi-Jie & Peng, Liang & Xia, Zhendong, 2007. "Interval estimation of value-at-risk based on GARCH models with heavy-tailed innovations," Journal of Econometrics, Elsevier, vol. 137(2), pages 556-576, April.
    4. Maddala,G. S. & Kim,In-Moo, 1999. "Unit Roots, Cointegration, and Structural Change," Cambridge Books, Cambridge University Press, number 9780521587822.
    5. Getmansky, Mila & Lo, Andrew W. & Makarov, Igor, 2004. "An econometric model of serial correlation and illiquidity in hedge fund returns," Journal of Financial Economics, Elsevier, vol. 74(3), pages 529-609, December.
    6. Ullah, Aman, 2004. "Finite Sample Econometrics," OUP Catalogue, Oxford University Press, number 9780198774488.
    7. Fung, William & Hsieh, David A, 1997. "Empirical Characteristics of Dynamic Trading Strategies: The Case of Hedge Funds," The Review of Financial Studies, Society for Financial Studies, vol. 10(2), pages 275-302.
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    Cited by:

    1. Xingxing Ye & Raphael Douady, 2018. "Systemic Risk Indicators Based on Nonlinear PolyModel," JRFM, MDPI, vol. 12(1), pages 1-24, December.
    2. Xingxing Ye & Raphaël Douady, 2019. "Risk and Financial Management Article Systemic Risk Indicators Based on Nonlinear PolyModel," Post-Print hal-02488592, HAL.
    3. Raphaël Douady, 2019. "Managing the Downside of Active and Passive Strategies: Convexity and Fragilities," Post-Print hal-02488589, HAL.
    4. Rachida Hennani & Michel Terraza, 2015. "Contributions of a noisy chaotic model to the stressed Value-at-Risk," Economics Bulletin, AccessEcon, vol. 35(2), pages 1262-1273.

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    Keywords

    Risk; Investment; Hedge Funds;
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