  This paper presents an adaptive version of the Hill estimator based on
Lespki's model selection method. This simple data-driven index selection method
is shown to satisfy an oracle inequality and is checked to achieve the lower
bound recently derived by Carpentier and Kim. In order to establish the oracle
inequality, we derive non-asymptotic variance bounds and concentration
inequalities for Hill estimators. These concentration inequalities are derived
from Talagrand's concentration inequality for smooth functions of independent
exponentially distributed random variables combined with three tools of Extreme
Value Theory: the quantile transform, Karamata's representation of slowly
varying functions, and R\'enyi's characterisation of the order statistics of
exponential samples. The performance of this computationally and conceptually
simple method is illustrated using Monte-Carlo simulations.
