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

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  • Rassoul, Abdelaziz

Abstract

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.

Suggested Citation

  • Rassoul, Abdelaziz, 2013. "Kernel-type estimator of the conditional tail expectation for a heavy-tailed distribution," Insurance: Mathematics and Economics, Elsevier, vol. 53(3), pages 698-703.
    • Handle: RePEc:eee:insuma:v:53:y:2013:i:3:p:698-703
    DOI: 10.1016/j.insmatheco.2013.09.004
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    References listed on IDEAS

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    Cited by:

    1. Nadezhda Gribkova & Ričardas Zitikis, 2019. "Weighted allocations, their concomitant-based estimators, and asymptotics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(4), pages 811-835, August.
    2. Francesca Greselin & Ričardas Zitikis, 2018. "From the Classical Gini Index of Income Inequality to a New Zenga-Type Relative Measure of Risk: A Modeller’s Perspective," Econometrics, MDPI, vol. 6(1), pages 1-20, January.

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