Existence and consistency of the maximum likelihood estimator for the extreme value index
Journal of Multivariate Analysis , Volume 100 - Issue 4 p. 794- 815
The paper is about the asymptotic properties of the maximum likelihood estimator for the extreme value index. Under the second order condition, Drees et al. [H. Drees, A. Ferreira, L. de Haan, On maximum likelihood estimation of the extreme value index, Ann. Appl. Probab. 14 (2004) 1179-1201] proved asymptotic normality for any solution of the likelihood equations (with shape parameter γ > - 1 / 2) that is not too far off the real value. But they did not prove that there is a solution of the equations satisfying the restrictions. In this paper, the existence is proved, even for γ > - 1. The proof just uses the domain of attraction condition (first order condition), not the second order condition. It is also proved that the estimator is consistent. When the second order condition is valid, following the current proof, the existence of a solution satisfying the restrictions in the above-cited reference is a direct consequence.