IDEAS home Printed from https://ideas.repec.org/p/hal/cesptp/hal-00666234.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Other versions of this item:

    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.
    2. Angelidis, Timotheos & Benos, Alexandros & Degiannakis, Stavros, 2004. "The Use of GARCH Models in VaR Estimation," MPRA Paper 96332, University Library of Munich, Germany.
    3. 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.
    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. Maddala,G. S. & Kim,In-Moo, 1999. "Unit Roots, Cointegration, and Structural Change," Cambridge Books, Cambridge University Press, number 9780521587822, September.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Raphaël Douady, 2019. "Managing the Downside of Active and Passive Strategies: Convexity and Fragilities," Post-Print hal-02488589, HAL.
    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. 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.
    4. Xingxing Ye & Raphael Douady, 2018. "Systemic Risk Indicators Based on Nonlinear PolyModel," JRFM, MDPI, vol. 12(1), pages 1-24, December.
    5. Xingxing Ye & Raphaël Douady, 2019. "Risk and Financial Management Article Systemic Risk Indicators Based on Nonlinear PolyModel," Post-Print hal-02488592, HAL.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Köksal, Bülent & Orhan, Mehmet, 2012. "Market risk of developed and developing countries during the global financial crisis," MPRA Paper 37523, University Library of Munich, Germany.
    2. Bali, Turan G. & Brown, Stephen J. & Caglayan, Mustafa O., 2019. "Upside potential of hedge funds as a predictor of future performance," Journal of Banking & Finance, Elsevier, vol. 98(C), pages 212-229.
    3. François-Éric Racicot & Raymond Théoret, 2022. "Tracking market and non-traditional sources of risks in procyclical and countercyclical hedge fund strategies under extreme scenarios: a nonlinear VAR approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-56, December.
    4. Agarwal, Vikas & Daniel, Naveen D. & Naik, Narayan Y., 2009. "Role of managerial incentives and discretion in hedge fund performance," CFR Working Papers 04-04, University of Cologne, Centre for Financial Research (CFR).
    5. Massa, Massimo & Jiao, Yawen, 2015. "Short Selling Meets Hedge Fund 13F: An Anatomy of Informed Demand," CEPR Discussion Papers 10471, C.E.P.R. Discussion Papers.
    6. Arjen Siegmann & Denitsa Stefanova, 2011. "Market Liquidity and Exposure of Hedge Funds," Tinbergen Institute Discussion Papers 11-150/2/DSF27, Tinbergen Institute.
    7. Nicolas Bollen, 2011. "The financial crisis and hedge fund returns," Review of Derivatives Research, Springer, vol. 14(2), pages 117-135, July.
    8. Agarwal, Vikas & Daniel, Naveen D. & Naik, Narayan Y., 2009. "Do hedge funds manage their reported returns?," CFR Working Papers 07-09, University of Cologne, Centre for Financial Research (CFR).
    9. Kooli, Maher & Zhang, Min, 2022. "Not only skill but also scale: Evidence from the hedge funds industry," International Review of Financial Analysis, Elsevier, vol. 83(C).
    10. Berger, Theo & Gençay, Ramazan, 2018. "Improving daily Value-at-Risk forecasts: The relevance of short-run volatility for regulatory quality assessment," Journal of Economic Dynamics and Control, Elsevier, vol. 92(C), pages 30-46.
    11. Bussière, Matthieu & Hoerova, Marie & Klaus, Benjamin, 2015. "Commonality in hedge fund returns: Driving factors and implications," Journal of Banking & Finance, Elsevier, vol. 54(C), pages 266-280.
    12. Angelidis, Timotheos & Degiannakis, Stavros, 2008. "Volatility forecasting: Intra-day versus inter-day models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 18(5), pages 449-465, December.
    13. Douglas Cumming & Na Dai, 2010. "Hedge Fund Regulation and Misreported Returns," European Financial Management, European Financial Management Association, vol. 16(5), pages 829-857, November.
    14. Mustafa Onur Caglayan & Sevan Ulutas, 2014. "Emerging Market Exposures and the Predictability of Hedge Fund Returns," Financial Management, Financial Management Association International, vol. 43(1), pages 149-180, March.
    15. Bill Ding & Hany A. Shawky, 2007. "The Performance of Hedge Fund Strategies and the Asymmetry of Return Distributions," European Financial Management, European Financial Management Association, vol. 13(2), pages 309-331, March.
    16. Degiannakis, Stavros & Xekalaki, Evdokia, 2007. "Assessing the Performance of a Prediction Error Criterion Model Selection Algorithm in the Context of ARCH Models," MPRA Paper 96324, University Library of Munich, Germany.
    17. Cao, Charles & Chen, Yong & Liang, Bing & Lo, Andrew W., 2013. "Can hedge funds time market liquidity?," Journal of Financial Economics, Elsevier, vol. 109(2), pages 493-516.
    18. Jiao, Yawen & Massa, Massimo & Zhang, Hong, 2016. "Short selling meets hedge fund 13F: An anatomy of informed demand," Journal of Financial Economics, Elsevier, vol. 122(3), pages 544-567.
    19. Loriana Pelizzon & Monica Billio & Mila Getmansky, 2008. "Non-Parametric Analysis of Hedge Fund Returns: New Insights from High Frequency Data," Working Papers 2008_11, Department of Economics, University of Venice "Ca' Foscari".
    20. Sadka, Ronnie, 2010. "Liquidity risk and the cross-section of hedge-fund returns," Journal of Financial Economics, Elsevier, vol. 98(1), pages 54-71, October.

    More about this item

    Keywords

    Risk; Investment; Hedge Funds;
    All these keywords.

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:cesptp:hal-00666234. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.