Improved Estimator of the Conditional Tail Expectation in the case of heavy-tailed losses
Mohamed Laidi, Abdelaziz Rassoul, Hamid Ould Rouis
Abstract
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Mohamed Laidi, Abdelaziz Rassoul, Hamid Ould Rouis
Abstract
Open-access reader
In this paper, we investigate the extreme-value methodology, to propose an improved estimator of the conditional tail expectation (CTE) for a loss distribution with a finite mean but infinite variance.The present work introduces a new estimator of the CTE based on the bias-reduced estimators of high quantile for heavy-tailed distributions. The asymptotic normality of the proposed estimator is established and checked, in a simulation study. Moreover, we compare, in terms of bias and mean squared error, our estimator with the known old estimator.
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In this paper, we investigate the extreme-value methodology, to propose an improved estimator of the conditional tail expectation (CTE) for a loss distribution with a finite mean but infinite variance.The present work introduces a new estimator of the CTE based on the bias-reduced estimators of high quantile for heavy-tailed distributions. The asymptotic normality of the proposed estimator is established and checked, in a simulation study. Moreover, we compare, in terms of bias and mean squared error, our estimator with the known old estimator.
Key concepts: Estimator, Minimum-variance unbiased estimator, Quantile, Mathematics, Trimmed estimator, Bias of an estimator, Efficient estimator, Mean squared error