A new regression-based tail index estimator

A new regression-based approach for the estimation of the tail
index of heavy-tailed distributions with several important properties is introduced.
First, it provides a bias reduction when compared to available
regression-based methods; second, it is resilient to the choice of the tail
length used for the estimation of the tail index; third, when the effect of
the slowly varying function at infinity of the Pareto distribution vanishes
slowly, it continues to perform satisfactorily; and fourth, it performs well
under dependence of unknown form. An approach to compute the asymptotic
variance under time dependence and conditional heteroskcedasticity
is also provided.