Lower bounds to the accuracy of tail index estimation

We deal with the problem of statistical inference on heavy-tailed distributions. We present non-uniform minimax lower bounds to the mean squared error (MSE) of tail index and extreme quantiles estimators. We show that the MSE of a robust estimator depends in a particular way on the sample size, the tail index and the tail constant, revealing the the natural normalising sequence.