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Nonparametric Risk Management With Generalized Hyperbolic Distributions

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  • Chen, Ying
  • Härdle, Wolfgang
  • Jeong, Seok-Oh

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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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of the American Statistical Association.

Volume (Year): 103 (2008)
Issue (Month): 483 ()
Pages: 910-923

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Handle: RePEc:bes:jnlasa:v:103:i:483:y:2008:p:910-923

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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. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
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Cited by:
  1. Ying Chen & Wolfgang Härdle & Vladimir Spokoiny, 2006. "GHICA - Risk Analysis with GH Distributions and Independent Components," SFB 649 Discussion Papers SFB649DP2006-078, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  2. Fengler, Matthias & Okhrin, Ostap, 2012. "Realized Copula," Economics Working Paper Series 1214, University of St. Gallen, School of Economics and Political Science.
  3. Borak, Szymon & Misiorek, Adam & Weron, Rafal, 2010. "Models for Heavy-tailed Asset Returns," MPRA Paper 25494, University Library of Munich, Germany.
  4. Javier Mencía & Enrique Sentana, 2009. "Multivariate location-scale mixtures of normals and mean-variance-skewness portfolio allocation," Banco de Espa�a Working Papers 0909, Banco de Espa�a.
  5. 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.
  6. 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.
  7. Pavel Cizek & Karel Komorad, 2005. "Implied Trinomial Trees," SFB 649 Discussion Papers SFB649DP2005-007, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  8. Adam Misiorek & Rafal Weron, 2010. "Heavy-tailed distributions in VaR calculations," HSC Research Reports HSC/10/05, Hugo Steinhaus Center, Wroclaw University of Technology.
  9. 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.

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