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 instability in the tails of financial time series.

翻译：我们针对弱相依时间序列中期望损失及相关风险度量的非参数估计量，提出了新颖的变点检验方法。在一般性假设条件下，本方法能够检测时间序列边缘分布尾部存在的多重结构变化。通过自标准化处理，我们避免了标准误差估计的常见问题。本方法的理论基础是在弱假设条件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，我们展示了该方法在检测与量化金融时间序列尾部不稳定性方面的实际应用价值。