Nonparametric Risk Management With Generalized Hyperbolic Distributions

被引：19

作者：

Chen, Ying ^{[1
]}

Haerdle, Wolfgang ^{[2
]}

Jeong, Seok-Oh ^{[3
]}

机构：

[1] Natl Univ Singapore, Dept Stat & Appl Probabil, Singapore 117543, Singapore

[2] Humboldt Univ, Ctr Appl Stat & Econ, D-10178 Berlin, Germany

[3] Hankuk Univ Foreign Studies, Dept Informat Stat, Mohyun 449791, Yongin, South Korea

来源：

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION | 2008年 / 103卷 / 483期

关键词：

Adaptive volatility estimation; Generalized hyperbolic distribution; Risk management; Value at risk;

D O I：

10.1198/016214507000001003

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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 apporpriately. 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.

引用

页码：910 / 923

页数：14

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