摘要翻译：
研究了弱相关时间序列中期望缺口和相关风险度量的非参数估计的变点检验和置信区间构造方法。我们工作的一个关键方面是检测时间序列边缘分布尾部一般多重结构变化的能力。与现有的使用尾翼指数等量检测尾翼结构变化的方法不同，我们的方法不需要对尾翼进行参数化建模，而是检测尾翼中更普遍的变化。此外，我们的方法是基于最近引入的时间序列自归一化技术，允许统计分析没有一致性标准误差估计的问题。我们的方法的理论基础是泛函中心极限定理，我们在较弱的假设下发展了这些定理。通过对标准普尔500和美国30年期国债收益率的实证研究，说明了我们的方法在金融危机期间通过金融时间序列的尾部检测和量化市场不稳定性方面的实际应用。
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英文标题：
《Change-Point Testing and Estimation for Risk Measures in Time Series》
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作者：
Lin Fan, Peter W. Glynn, Markus Pelger
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最新提交年份：
2018
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
  We investigate methods of change-point testing and confidence interval construction for nonparametric estimators of expected shortfall and related risk measures in weakly dependent time series. A key aspect of our work is the ability to detect general multiple structural changes in the tails of time series marginal distributions. Unlike extant approaches for detecting tail structural changes using quantities such as tail index, our approach does not require parametric modeling of the tail and detects more general changes in the tail. Additionally, our methods are based on the recently introduced self-normalization technique for time series, allowing for statistical analysis without the issues of consistent standard error estimation. The theoretical foundation for our methods are functional central limit theorems, which we develop under weak assumptions. An empirical study of S&P 500 returns and US 30-Year Treasury bonds illustrates the practical use of our methods in detecting and quantifying market instability via the tails of financial time series during times of financial crisis.
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PDF链接：
https://arxiv.org/pdf/1809.02303