High-quantile regression for tail-dependent time series
尾部依赖时间序列的高分位数回归AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确研究有序列依赖的时间序列及随样本量变化的高分位数回归，分别对应时间序列与分位数方法。
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尾部依赖时间序列的高分位数回归
- 研究问题
- 当分位数水平随样本量升高、时间序列在尾部也存在序列依赖时，如何建立高分位数回归估计量的渐近推断？
- 方法与思路
- 作者引入尾部对抗稳定性条件，为具有尾部序列依赖、但未必强混合的时间序列建立高分位数回归极限理论。
- 材料陈述的贡献
- 摘要称该条件提供了可解释的尾部依赖极限理论框架，数值实验显示忽略尾部依赖可能产生误导性的 p 值。
- 阅读关注点
- 应核对高分位水平随样本量变化的速度、稳定性条件如何验证，以及尾部依赖进入极限分布和推断的方式。
- 阅读边界
- 仅据所给 Crossref 摘要，无法确认稳定性条件覆盖哪些过程；文中尾部现象并未被明确限定为金融风险。
原文摘要
Summary Quantile regression is a popular and powerful method for studying the effect of regressors on quantiles of a response distribution. However, existing results on quantile regression were mainly developed for cases in which the quantile level is fixed, and the data are often assumed to be independent. Motivated by recent applications, we consider the situation where (i) the quantile level is not fixed and can grow with the sample size to capture the tail phenomena, and (ii) the data are no longer independent, but collected as a time series that can exhibit serial dependence in both tail and non-tail regions. To study the asymptotic theory for high-quantile regression estimators in the time series setting, we introduce a tail adversarial stability condition, which had not previously been described, and show that it leads to an interpretable and convenient framework for obtaining limit theorems for time series that exhibit serial dependence in the tail region, but are not necessarily strongly mixing. Numerical experiments are conducted to illustrate the effect of tail dependence on high-quantile regression estimators, for which simply ignoring the tail dependence may yield misleading $p$-values.