Kernel-type estimator of the conditional tail expectation for a heavy-tailed distribution

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• 作者：Abdelaziz Rassoul
• 关键词：Risk measure ; CTE ; Heavy tails ; Kernel ; Hill estimator ; Extreme quantile ; Reduced bias
• 刊名：Insurance: Mathematics and Economics
• 出版年：November, 2013
• 年：2013
• 卷：53
• 期：3
• 页码：698-703
• 全文大小：406 K

文摘

In this paper, we are interested in the generalization and improvement of the estimator of the conditional tail expectation (CTE) for a heavy-tailed distribution when the second moment is infinite. It is well known that classical estimators of the CTE are seriously biased under the second-order regular variation framework. To reduce the bias, many authors proposed the use of so-called second-order reduced bias estimators for both first-order and second-order tail parameters. In this work, we have generalized a kernel-type estimator, and we present a number of results on its distributional behavior and compare its performance with the performance of other estimators.