We address the problem of estimating the expected shortfall risk of a
financial loss using a finite number of i.i.d. data. It is well known that the
classical plug-in estimator suffers from poor statistical performance when
faced with (heavy-tailed) distributions that are commonly used in financial
contexts. Further, it lacks robustness, as the modification of even a single
data point can cause a significant distortion. We propose a novel procedure for
the estimation of the expected shortfall and prove that it recovers the best
possible statistical properties (dictated by the central limit theorem) under
minimal assumptions and for all finite numbers of data. Further, this estimator
is adversarially robust: even if a (small) proportion of the data is
maliciously modified, the procedure continuous to optimally estimate the true
expected shortfall risk. We demonstrate that our estimator outperforms the
classical plug-in estimator through a variety of numerical experiments across a
range of standard loss distributions.