A note on nonparametric estimation of bivariate tail dependence

IF 1.3 Q2 STATISTICS & PROBABILITY

Statistics & Risk Modeling Pub Date : 2014-01-28 DOI:10.1515/strm-2013-1143

Axel Bücher

引用次数: 6

Abstract

Abstract Nonparametric estimation of tail dependence can be based on a standardization of the marginals if their cumulative distribution functions are known. In this paper it is shown to be asymptotically more efficient if the additional knowledge of the marginals is ignored and estimators are based on ranks. The discrepancy between the two estimators is shown to be substantial for the popular Clayton and Gumbel–Hougaard models. A brief simulation study indicates that the asymptotic conclusions transfer to finite samples.

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关于二元尾相关的非参数估计的一个注记

摘要如果尾相关性的累积分布函数已知，则可以基于边缘的标准化进行尾相关性的非参数估计。在本文中，如果忽略边际的附加知识和基于秩的估计量，则证明了它是渐近地更有效的。对于流行的Clayton和Gumbel-Hougaard模型，两个估计器之间的差异显示为实质性的。一个简短的模拟研究表明，渐近结论转移到有限样本。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Statistics & Risk Modeling STATISTICS & PROBABILITY-

CiteScore

1.80

自引率

6.70%

发文量

期刊介绍： Statistics & Risk Modeling (STRM) aims at covering modern methods of statistics and probabilistic modeling, and their applications to risk management in finance, insurance and related areas. The journal also welcomes articles related to nonparametric statistical methods and stochastic processes. Papers on innovative applications of statistical modeling and inference in risk management are also encouraged. Topics Statistical analysis for models in finance and insurance Credit-, market- and operational risk models Models for systemic risk Risk management Nonparametric statistical inference Statistical analysis of stochastic processes Stochastics in finance and insurance Decision making under uncertainty.