The use of expectiles in risk management contexts has recently gathered substantial momentum because of their excellent axiomatic and probabilistic properties. While expectile estimation at central levels already has a substantial history, expectile estimation at extreme levels has so far only been considered when the underlying distribution has a heavy right tail. This article focuses on the challenging short-tailed setting when the distribution of the variable of interest has a negative extreme value index and is bounded to the right. We derive an asymptotic expansion of extreme expectiles in this context under a general second-order extreme value condition. This asymptotic expansion makes it possible to study two semiparametric estimators of extreme expectiles, whose asymptotic properties we obtain in a general model of strictly stationary but weakly dependent observations. A simulation study and real data analysis illustrate the performance of the proposed statistical techniques.

翻译：最近,在风险管理背景下对预期值的使用由于其极好的不言而喻和概率性特征而取得了巨大的势头。虽然中央一级的预期值估算已经具有相当的历史,但迄今为止,只有在基本分布有极右尾部时才考虑极端水平的预期值估算。本条侧重于当利害变量的分布呈负极端值指数且与右边相连时具有挑战性的短期环境。在一般的二阶极端值条件下,我们从这一背景下获得的极端预期值的无症状扩张。这种无症状的扩展使得有可能研究两个极端预期值的半参数估测器,我们从一个严格固定但依赖性强的观测总模型中获得了这些预测值的半参数。模拟研究和真实数据分析显示了拟议统计技术的绩效。