Nonparametric Risk Management With Generalized Hyperbolic Distributions

Ying Chen · Wolfgang Karl Härdle · Seok‐Oh Jeong

51 citations0/0 files storedJournal of the American Statistical Association

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Financial Risk and Volatility Modeling Stochastic processes and financial applications Probability and Risk Models

Abstract

In this article we propose the generalized hyperbolic adaptive volatility (GHADA) risk management model based on the generalized hyperbolic (GH) distribution and on a nonparametric adaptive methodology. Compared with the normal distribution, the GH distribution has semiheavy tails and represents the financial risk factors more appropriately. Nonparametric adaptive methodology has the desirable property of being able to estimate homogeneous volatility over a short time interval and reflects a sudden change in the volatility process. For the German mark/U.S. dollar exchange rate and German bank portfolio data, the proposed GHADA model provides more accurate Value at risk calculations than the models with assumptions of the normal and t distributions.

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References

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2009The econometrics of mean‐variance efficiency tests: a survey 2011Expected shortfall for distributions in finance 2013Independent Factor Autoregressive Conditional Density Model 2009GHICA — Risk analysis with GH distributions and independent components 2010Testing Non-Linear Dependence in the Hedge Fund Industry 2012Managing Risk with a Realized Copula Parameter

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