PORT Hill and Moment Estimators for Heavy-Tailed Models

Gomes, M. Ivette; Alves, M. Isabel Fraga; Santos, Paulo Araújo

http://hdl.handle.net/10400.15/2992

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Authors

Gomes, M. Ivette

Alves, M. Isabel Fraga

Santos, Paulo Araújo

Abstract(s)

In this article, we use the peaks over random threshold (PORT)-methodology, and consider Hill and moment PORT-classes of extreme value index estimators. These classes of estimators are invariant not only to changes in scale, like the classical Hill and moment estimators, but also to changes in location. They are based on the sample of excesses over a random threshold, the order statistic X[np]+1:n, 0 ≤ p < 1, being p a tuning parameter, which makes them highly flexible. Under convenient restrictions on the underlying model, these classes of estimators are consistent and asymptotically normal for adequate values of k, the number of top order statistics used in the semi-parametric estimation of the extreme value index γ. In practice, there may however appear a stability around a value distant from the target γ when the minimum is chosen for the random threshold, and attention is drawn for the danger of transforming the original data through the subtraction of the minimum. A new bias-corrected moment estimator is also introduced. The exact performance of the new extreme value index PORT-estimators is compared, through a large-scale Monte-Carlo simulation study, with the original Hill and moment estimators, the bias-corrected moment estimator, and one of the minimum-variance reduced-bias (MVRB) extreme value index estimators recently introduced in the literature. As an empirical example we estimate the tail index associated to a set of real data from the field of finance.

Keywords

Extreme value index Monte Carlo simulation Reduced-bias estimation Sample of excesses Semi-parametric estimation Statistics of extremes Statistics of extremes

URI

http://hdl.handle.net/10400.15/2992

Citation

Gomes, M. I., Alves, M. I., & Santos, P. (2008). PORT Hill and moment estimators for Heavy-Tailed Models. Communications in Statistics : Simulation & Computation, 37(7), 1281–1306. doi: 10.1080/03610910802050910