标题： 尾过程推断及其在金融时间序列建模中的应用 显示英文标题

标题： Inference on the tail process with application to financial time series modelling

摘要： 为了推断重尾马尔可夫链中的序列极值依赖性，Drees、Segers 和 Warcho{\l }[Extremes (2015) 第 18 卷，第 369–402 页] 提出了非参数谱尾过程估计量。 该方法可以推广到更一般的平稳正则变化时间序列设定。 通过聚类函数的 empirical process 理论，得到了这些估计量的大样本分布。 通过蒙特卡洛模拟评估了这些估计量的有限样本性能。 此外，采用了两种不同的自助法方案，用于获得预渐近谱尾过程的置信区间：即平稳自助法和乘数块自助法。 这些估计量被应用于股票价格数据以研究正向和负向冲击的持久性。 显示英文摘要

摘要： To draw inference on serial extremal dependence within heavy-tailed Markov chains, Drees, Segers and Warcho{\l} [Extremes (2015) 18, 369--402] proposed nonparametric estimators of the spectral tail process. The methodology can be extended to the more general setting of a stationary, regularly varying time series. The large-sample distribution of the estimators is derived via empirical process theory for cluster functionals. The finite-sample performance of these estimators is evaluated via Monte Carlo simulations. Moreover, two different bootstrap schemes are employed which yield confidence intervals for the pre-asymptotic spectral tail process: the stationary bootstrap and the multiplier block bootstrap. The estimators are applied to stock price data to study the persistence of positive and negative shocks.