We propose novel methods for change-point testing for nonparametric estimators of expected shortfall and related risk measures in weakly dependent time series. We can detect general multiple structural changes in the tails of marginal distributions of time series under general assumptions. Self-normalization allows us to avoid the issues of standard error estimation. The theoretical foundations for our methods are functional central limit theorems, which we develop under weak assumptions. An empirical study of S&P 500 and US Treasury bond returns illustrates the practical use of our methods in detecting and quantifying market instability via the tails of financial time series.

翻译：我们针对弱依赖时间序列中期望损失及相关风险度量的非参数估计量，提出新的变点检验方法。在一般假设条件下，我们能够检测时间序列边际分布尾部中常见的多重结构性变化。自归一化方法使我们能够避免标准误差估计中的问题。我们方法的理论基础是在弱假设下建立的功能性中心极限定理。通过对标普500指数和美国国债收益率的实证研究，展示了我们的方法在通过金融时间序列尾部检测和量化市场不稳定性方面的实际应用价值。