We consider paired and two-sample break-detection procedures for vectorial observations and multivariate time series. The new methods involve L2-type criteria based on empirical characteristic functions and are easy to compute regardless of dimension. We obtain asymptotic results that allow for application of the methods to a wide range of settings involving on-line as well as retrospective circumstances with dependence between the two time series as well as with dependence within each series. In the ensuing Monte Carlo study the new detection methods are implemented by means of resampling procedures which are properly adapted to the type of data at hand, be it independent or paired, autoregressive or GARCH structured, medium or heavy-tailed. The new methods are also applied on a real dataset from the financial sector over a time period which includes the Brexit referendum.

中文翻译：

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多元时间序列的变点方法：成对矢量观测

我们考虑了矢量观察和多元时间序列的配对和双样本中断检测程序。新方法涉及基于经验特征函数的 L2 类型标准，并且无论维度如何都易于计算。我们获得了渐近的结果，允许将这些方法应用于广泛的设置，包括在线以及回顾性环境，两个时间序列之间的依赖以及每个序列内的依赖。在随后的蒙特卡罗研究中，新的检测方法是通过重新采样程序实现的，这些程序适合手头的数据类型，无论是独立的还是成对的、自回归的或 GARCH 结构的、中尾或重尾的。