Beta kernel quantile estimators of heavy-tailed loss distributions

被引：0
|
作者：
Arthur Charpentier
Abder Oulidi
机构：
[1] CREM-Université Rennes 7,
[2] IMA,undefined
来源：
Statistics and Computing | 2010年 / 20卷
关键词：
Beta kernels; distribution; Loss distributions; Quantile estimation; Transformed kernel; Value-at-risk;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper we suggest several nonparametric quantile estimators based on Beta kernel. They are applied to transformed data by the generalized Champernowne distribution initially fitted to the data. A Monte Carlo based study has shown that those estimators improve the efficiency of the traditional ones, not only for light tailed distributions, but also for heavy tailed, when the probability level is close to 1. We also compare these estimators with the Extreme Value Theory Quantile applied to Danish data on large fire insurance losses.
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页码：35 / 55
页数：20
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