Title: Data and uncertainty in extreme risks - a nonlinear expectations approach,
Abstract: Estimation of tail quantities, such as expected shortfall or Value at Risk,
is a difficult problem. We show how the theory of nonlinear expectations, in
particular the Data-robust expectation introduced in [5], can assist in the
quantification of statistical uncertainty for these problems. However, when we
are in a heavy-tailed context (in particular when our data are described by a
Pareto distribution, as is common in much of extreme value theory), the theory
of [5] is insufficient, and requires an additional regularization step which we
introduce. By asking whether this regularization is possible, we obtain a
qualitative requirement for reliable estimation of tail quantities and risk
measures, in a Pareto setting.