Nonparametric Risk Management With Generalized Hyperbolic Distributions
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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Volume (Year): 103 (2008)
Issue (Month): 483 ()
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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Tim Bollerslev, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
EERI Research Paper Series
EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
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"Multivariate Stochastic Variance Models,"
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Wiley Blackwell, vol. 61(2), pages 247-64, April.
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