论文标题
对依赖度量的多个断点的一致估计
Consistent Estimation of Multiple Breakpoints in Dependence Measures
论文作者
论文摘要
本文提出了不同的方法，以始终如一地检测基于Copula的依赖度量中的多次休息，主要集中于Spearman的$ρ$。领先的模型是一个因子模型模型，因为其在高维度中分析数据的有用性。从经典的二进制分割开始，还考虑了最新的野生二进制分割（WBS）和基于信息标准的程序。对于所有过程，证明了断点位置的估计量以及断裂数量的一致性。蒙特卡洛模拟表明，在许多情况下，WBS在许多情况下表现最好，但并非如此。对最近的Euro Stoxx 50数据的真实数据应用程序揭示了该程序的有用性。
This paper proposes different methods to consistently detect multiple breaks in copula-based dependence measures, mainly focusing on Spearman's $ρ$. The leading model is a factor copula model due to its usefulness for analyzing data in high dimensions. Starting with the classical binary segmentation, also the more recent wild binary segmentation (WBS) and a procedure based on an information criterion are considered. For all procedures, consistency of the estimators for the location of the breakpoints as well as the number of breaks is proved. Monte Carlo simulations indicate that WBS performs best in many, but not in all, situations. A real data application on recent Euro Stoxx 50 data reveals the usefulness of the procedures.
