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Nonparametric Risk Management With Generalized Hyperbolic Distributions

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

In this paper we propose the GHADA risk management model that is based on the gener- alized hyperbolic (GH) distribution and on a nonparametric adaptive methodology. Com- pared to the normal distribution, the GH distribution possesses semi-heavy tails and repre- sents the financial risk factors more appropriately. The nonparametric adaptive methodol- ogy 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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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. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
  2. Harvey, Andrew & Ruiz, Esther & Shephard, Neil, 1994. "Multivariate Stochastic Variance Models," Review of Economic Studies, Wiley Blackwell, vol. 61(2), pages 247-64, April.
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