  The use of expectiles in risk management has recently gathered remarkable
momentum due to their excellent axiomatic and probabilistic properties. In
particular, the class of elicitable law-invariant coherent risk measures only
consists of expectiles. While the theory of expectile estimation at central
levels is substantial, tail estimation at extreme levels has so far only been
considered when the tail of the underlying distribution is heavy. This article
is the first work to handle the short-tailed setting where the loss (e.g.
negative log-returns) distribution of interest is bounded to the right and the
corresponding extreme value index is negative. We derive an asymptotic
expansion of tail expectiles in this challenging context under a general
second-order extreme value condition, which allows to come up with two
semiparametric estimators of extreme expectiles, and with their asymptotic
properties in a general model of strictly stationary but weakly dependent
observations. A simulation study and a real data analysis from a forecasting
perspective are performed to verify and compare the proposed competing
estimation procedures.
