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The StressVaR: 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

Abstract

In this paper we introduce a novel 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 3 step procedure whose main components we describe in relative detail. Its principle 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. We then use the risk profile and the very long and rich history of the factors to asses the possible impact of known past crises on the funds, unveiling their hidden risks and so called "black swans" (Taleb [2007]). In backtests using data of 1060 hedge funds we demonstrate that the SVaR has better or comparable properties than several common VaR measures-shows less VaR exceptions and, perhaps even more importantly, in case of an exception, by smaller amounts. The ultimate test of the StressVaR however, is in its usage as a fund allocating tool. By simulating a realistic investment in a portfolio of hedge funds, we show that the portfolio constructed using the StressVaR on average outperforms both the market and the portfolios constructed using common VaR measures. For the period from are even more impressive. The SVaR portfolio outperforms the market by 20%, and the best competing measure by 4%.

Suggested Citation

  • Cyril Coste & Raphaël Douady & Ilija I Zovko, 2010. "The StressVaR: A New Risk Concept for Extreme Risk and Fund Allocation," Post-Print hal-02488591, HAL.
    • Handle: RePEc:hal:journl:hal-02488591
    DOI: 10.3905/jai.2011.13.3.010
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    1. 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.
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    3. Angelidis, Timotheos & Benos, Alexandros & Degiannakis, Stavros, 2004. "The Use of GARCH Models in VaR Estimation," MPRA Paper 96332, University Library of Munich, Germany.
    4. 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.
    5. Ullah, Aman, 2004. "Finite Sample Econometrics," OUP Catalogue, Oxford University Press, number 9780198774488.
    6. 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. 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.
    3. Xingxing Ye & Raphaël Douady, 2019. "Risk and Financial Management Article Systemic Risk Indicators Based on Nonlinear PolyModel," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-02488592, HAL.
    4. Raphaël Douady, 2019. "Managing the Downside of Active and Passive Strategies: Convexity and Fragilities," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-02488589, HAL.
    5. Siqiao Zhao & Zhikang Dong & Zeyu Cao & Raphael Douady, 2024. "Hedge Fund Portfolio Construction Using PolyModel Theory and iTransformer," Papers 2408.03320, arXiv.org, revised Aug 2024.

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