Kernel-type estimator of the reinsurance premium for heavy-tailed loss distributions

DOI10.1016/j.insmatheco.2014.08.006zbMath1306.91069OpenAlexW2037669780MaRDI QIDQ2514606

Lazhar Benkhelifa

Publication date: 3 February 2015

Published in: Insurance Mathematics \& Economics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.insmatheco.2014.08.006

zbMATH Keywords

bias reductionheavy tailskernel estimatorHill estimatorproportional hazard premiumreinsurance treatyextreme quantile

Mathematics Subject Classification ID

Uses Software

• R

Cites Work

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• Reiss and Thomas' automatic selection of the number of extremes
• Kernel estimates of the tail index of a distribution
• Weighted empirical and quantile processes
• A simple general approach to inference about the tail of a distribution
• On exponential representations of log-spacings of extreme order statistics
• Tail index estimation and an exponential regression model
• Bias reduction and explicit semi-parametric estimation of the tail index
• Estimating a tail exponent by modelling departure from a Pareto distribution
• Reduced-bias estimator of the Proportional Hazard Premium for heavy-tailed distributions
• On univariate extreme value statistics and the estimation of reinsurance premiums
• Erratum to: ‘Statistical estimate of the proportional hazard premium of loss’
• Statistical estimate of the proportional hazard premium of loss
• Using a bootstrap method to choose the sample fraction in tail index estimation
• Confidence intervals for the tail index

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