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Nonparametric Risk Management with Generalized Hyperbolic Distributions

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  • 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.

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File URL: http://sfb649.wiwi.hu-berlin.de/papers/pdf/SFB649DP2005-001.pdf
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Bibliographic Info

Paper provided by Sonderforschungsbereich 649, Humboldt University, Berlin, Germany in its series SFB 649 Discussion Papers with number SFB649DP2005-001.

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Length: 33 pages
Date of creation: Oct 2004
Date of revision: Aug 2005
Handle: RePEc:hum:wpaper:sfb649dp2005-001

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

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References

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  1. Harvey, Andrew & Ruiz, Esther & Shephard, Neil, 1994. "Multivariate Stochastic Variance Models," Review of Economic Studies, Wiley Blackwell, vol. 61(2), pages 247-64, April.
  2. Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
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Citations

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Cited by:
  1. Mencía, Javier & Sentana, Enrique, 2009. "Multivariate location-scale mixtures of normals and mean-variance-skewness portfolio allocation," Journal of Econometrics, Elsevier, vol. 153(2), pages 105-121, December.
  2. Stefan Kassberger & Rüdiger Kiesel, 2006. "A fully parametric approach to return modelling and risk management of hedge funds," Financial Markets and Portfolio Management, Springer, vol. 20(4), pages 472-491, December.
  3. Matthias R. Fengler & Ostap Okhrin, 2012. "Realized Copula," SFB 649 Discussion Papers SFB649DP2012-034, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  4. Adam Misiorek & Rafal Weron, 2010. "Heavy-tailed distributions in VaR calculations," HSC Research Reports HSC/10/05, Hugo Steinhaus Center, Wroclaw University of Technology.
  5. Borak, Szymon & Misiorek, Adam & Weron, Rafal, 2010. "Models for Heavy-tailed Asset Returns," MPRA Paper 25494, University Library of Munich, Germany.
  6. Pavel Cizek & Karel Komorad, 2005. "Implied Trinomial Trees," SFB 649 Discussion Papers SFB649DP2005-007, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  7. 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.
  8. 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.
  9. Michal Skorepa, 2014. "Concurrent Capital Buffers in a Banking Group," Occasional Publications - Chapters in Edited Volumes, in: CNB Financial Stability Report 2013/2014, chapter 0, pages 128-136 Czech National Bank, Research Department.
  10. Alexios Ghalanos & Eduardo Rossi & Giovanni Urga, 2012. "Independent Factor Autoregressive Conditional Density Model," DEM Working Papers Series 021, University of Pavia, Department of Economics and Management.

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