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Nonparametric Risk Management with Generalized Hyperbolic Distributions

Author

Listed:
  • Ying Chen
  • Wolfgang Härdle
  • Seok-Oh Jeong

Abstract

In this paper we propose the GHADA risk management model that is based on the generalized hyperbolic (GH) distribution and on a nonparametric adaptive methodology. Compared to the normal distribution, the GH distribution possesses semi-heavy tails and represents the financial risk factors more appropriately. The nonparametric adaptive methodology has the desirable property of estimating homogeneous volatility in a short time interval. For DEM/USD exchange rate data and a German bank portfolio data the proposed GHADA model provides more accurate value at risk calculation than the traditional model based on the normal distribution. All calculations and simulations are done with XploRe.

Suggested Citation

  • Ying Chen & Wolfgang Härdle & Seok-Oh Jeong, 2004. "Nonparametric Risk Management with Generalized Hyperbolic Distributions," SFB 649 Discussion Papers SFB649DP2005-001, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany, revised Aug 2005.
  • Handle: RePEc:hum:wpaper:sfb649dp2005-001
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    File URL: http://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2005-001.pdf
    File Function: Revised version, 2005
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    References listed on IDEAS

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    1. Andrew Harvey & Esther Ruiz & Neil Shephard, 1994. "Multivariate Stochastic Variance Models," Review of Economic Studies, Oxford University Press, vol. 61(2), pages 247-264.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
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    Citations

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    Cited by:

    1. Pavel Cizek & Karel Komorad, 2005. "Implied Trinomial Trees," SFB 649 Discussion Papers SFB649DP2005-007, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    2. Chen, Ying & Härdle, Wolfgang & Spokoiny, Vladimir, 2010. "GHICA -- Risk analysis with GH distributions and independent components," Journal of Empirical Finance, Elsevier, vol. 17(2), pages 255-269, March.
    3. Ying Chen & Wolfgang Härdle & Vladimir Spokoiny, 2005. "Portfolio Value at Risk Based on Independent Components Analysis," SFB 649 Discussion Papers SFB649DP2005-060, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    4. Stefan Kassberger & Rüdiger Kiesel, 2006. "A fully parametric approach to return modelling and risk management of hedge funds," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 20(4), pages 472-491, December.
    5. Yu, Yaming, 2017. "On normal variance–mean mixtures," Statistics & Probability Letters, Elsevier, vol. 121(C), pages 45-50.

    More about this item

    Keywords

    adaptive volatility estimation; generalized hyperbolic distribution; value at risk; risk management;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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