IDEAS home Printed from https://ideas.repec.org/p/drm/wpaper/2009-46.html
   My bibliography  Save this paper

Evaluation of Hedge Fund Returns Value at Risk Using GARCH Models

Author

Listed:
  • Sabrina Khanniche

Abstract

The aim of this research paper is to evaluate hedge fund returns Value-at-Risk by using GARCH models. To perform the empirical analysis, one uses the HFRX daily performance hedge fund strategy subindexes and spans the period March 2003 – March 2008. I found that skewness and kurtosis are substantial in the hedge fund returns distribution and the clustering phenomenon is pointed out. These features suggest the use of GARCH models to model the volatility of hedge fund return indexes. Hedge fund return conditional variances are estimated by using linear models (GARCH) and non-linear asymmetric models (EGARCH and TGARCH). Performance of several Value at Risk models is compared; the Gaussian VaR, the student VaR, the cornish fisher VaR, the normal GARCH-type VaR, the student GARCH-type VaR and the cornish fisher GARCH-type VaR. Our results demonstrate that the normal VaR underestimates accurate hedge fund risks while the student and the cornish fisher GARCH-type VaR are more reliable to estimate the potential maximum loss of hedge funds.

Suggested Citation

  • Sabrina Khanniche, 2009. "Evaluation of Hedge Fund Returns Value at Risk Using GARCH Models," EconomiX Working Papers 2009-46, University of Paris Nanterre, EconomiX.
  • Handle: RePEc:drm:wpaper:2009-46
    as

    Download full text from publisher

    File URL: http://economix.fr/pdf/dt/2009/WP_EcoX_2009-46.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Darryll Hendricks, 1996. "Evaluation of value-at-risk models using historical data," Economic Policy Review, Federal Reserve Bank of New York, vol. 2(Apr), pages 39-69.
    2. Sean D. Campbell, 2005. "A review of backtesting and backtesting procedures," Finance and Economics Discussion Series 2005-21, Board of Governors of the Federal Reserve System (U.S.).
    3. 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.).
    4. Rabemananjara, R & Zakoian, J M, 1993. "Threshold Arch Models and Asymmetries in Volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(1), pages 31-49, Jan.-Marc.
    5. Engle, Robert F. & Manganelli, Simone, 2001. "Value at risk models in finance," Working Paper Series 75, European Central Bank.
    6. Chris Brooks & Harry. M Kat, 2001. "The Statistical Properties of Hedge Fund Index Returns," ICMA Centre Discussion Papers in Finance icma-dp2001-09, Henley Business School, University of Reading.
    7. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78, World Scientific Publishing Co. Pte. Ltd..
    8. 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.
    9. Adrian Blundell-Wignall, 2007. "Structured Products: Implications for Financial Markets," Financial Market Trends, OECD Publishing, vol. 2007(2), pages 27-57.
    10. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    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. Christian Manicaro & Joseph Falzon, 2017. "Hedge funds risk and connectedness," Journal of Asset Management, Palgrave Macmillan, vol. 18(4), pages 295-316, July.

    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. Gonzalo Cortazar & Alejandro Bernales & Diether Beuermann, 2005. "Methodology and Implementation of Value-at-Risk Measures in Emerging Fixed-Income Markets with Infrequent Trading," Finance 0512030, University Library of Munich, Germany.
    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. de Araújo, André da Silva & Garcia, Maria Teresa Medeiros, 2013. "Risk contagion in the north-western and southern European stock markets," Journal of Economics and Business, Elsevier, vol. 69(C), pages 1-34.
    4. Carol Alexander & Emese Lazar & Silvia Stanescu, 2011. "Analytic Approximations to GARCH Aggregated Returns Distributions with Applications to VaR and ETL," ICMA Centre Discussion Papers in Finance icma-dp2011-08, Henley Business School, University of Reading.
    5. Stavroyiannis, S. & Makris, I. & Nikolaidis, V. & Zarangas, L., 2012. "Econometric modeling and value-at-risk using the Pearson type-IV distribution," International Review of Financial Analysis, Elsevier, vol. 22(C), pages 10-17.
    6. Felipe de Oliveira & Sinézio Fernandes Maia, 2017. "Volatility Forecasting before the Subprime Crisis," EcoMod2017 10376, EcoMod.
    7. Szubzda Filip & Chlebus Marcin, 2019. "Comparison of Block Maxima and Peaks Over Threshold Value-at-Risk models for market risk in various economic conditions," Central European Economic Journal, Sciendo, vol. 6(53), pages 70-85, January.
    8. Gaglianone, Wagner Piazza & Lima, Luiz Renato & Linton, Oliver & Smith, Daniel R., 2011. "Evaluating Value-at-Risk Models via Quantile Regression," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 150-160.
    9. Buczyński Mateusz & Chlebus Marcin, 2018. "Comparison of Semi-Parametric and Benchmark Value-At-Risk Models in Several Time Periods with Different Volatility Levels," Financial Internet Quarterly (formerly e-Finanse), Sciendo, vol. 14(2), pages 67-82, June.
    10. Chrétien, Stéphane & Coggins, Frank, 2010. "Performance and conservatism of monthly FHS VaR: An international investigation," International Review of Financial Analysis, Elsevier, vol. 19(5), pages 323-333, December.
    11. CHEN, Cathy W.S. & WENG, Monica M.C. & WATANABE, Toshiaki & 渡部, 渡部, 2015. "Employing Bayesian Forecasting of Value-at-Risk to Determine an Appropriate Model for Risk Management," Discussion paper series HIAS-E-16, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    12. Van Cauwenberge, Annelies & Vancauteren, Mark & Braekers, Roel & Vandemaele, Sigrid, 2019. "International trade, foreign direct investments, and firms’ systemic risk : Evidence from the Netherlands," Economic Modelling, Elsevier, vol. 81(C), pages 361-386.
    13. Guermat, Cherif & Harris, Richard D. F., 2002. "Forecasting value at risk allowing for time variation in the variance and kurtosis of portfolio returns," International Journal of Forecasting, Elsevier, vol. 18(3), pages 409-419.
    14. Kulp-Tåg, Sofie, 2007. "An Empirical Investigation of Value-at-Risk in Long and Short Trading Positions," Working Papers 526, Hanken School of Economics.
    15. Med Imen Gallali & Raggad Zahraa, 2012. "Evaluation of VaR models' forecasting performance: the case of oil markets," International Journal of Financial Services Management, Inderscience Enterprises Ltd, vol. 5(3), pages 197-215.
    16. Anastassios A. Drakos & Georgios P. Kouretas & Leonidas P. Zarangas, 2010. "Forecasting financial volatility of the Athens stock exchange daily returns: an application of the asymmetric normal mixture GARCH model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 331-350.
    17. Vincenzo Candila & Giampiero M. Gallo & Lea Petrella, 2020. "Mixed--frequency quantile regressions to forecast Value--at--Risk and Expected Shortfall," Papers 2011.00552, arXiv.org, revised Mar 2023.
    18. LeBaron, Blake, 2003. "Non-Linear Time Series Models in Empirical Finance,: Philip Hans Franses and Dick van Dijk, Cambridge University Press, Cambridge, 2000, 296 pp., Paperback, ISBN 0-521-77965-0, $33, [UK pound]22.95, [," International Journal of Forecasting, Elsevier, vol. 19(4), pages 751-752.
    19. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654.
    20. Timotheos Angelidis & Alexandros Benos & Stavros Degiannakis, 2007. "A robust VaR model under different time periods and weighting schemes," Review of Quantitative Finance and Accounting, Springer, vol. 28(2), pages 187-201, February.

    More about this item

    Keywords

    Hedge Fund; Value at Risk; GARCH models.;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:drm:wpaper:2009-46. 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: Valerie Mignon (email available below). General contact details of provider: https://edirc.repec.org/data/modemfr.html .

    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.