Abstract
In an insurance context, consider {Xn, n ≥ 1} random claim sizes with common distribution function F and {N(t), t ≥ 0} an integer valued stochastic process tfiat counts the number of claims occurring during the time interval [0, t]. Based on the number of near-extremes which are the observations Xi near the largest or the mth largest observation, we derive in this article a strongly consistent estimator of upper tails of X1. Furthermore, estimators for both the tail index and the upper endpoint are introduced when F is a generalized Pareto distribution. Asymptotic normal law for the proposed estimators is additionally presented.
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