Consistent Estimation of Multiple Breakpoints in Dependence Measures
提出多种方法一致检测基于Copula的依赖度量中的多个断点，包括经典二元分割和野二元分割，并证明估计量的一致性，通过蒙特卡洛模拟和Euro Stoxx 50数据验证。
This article proposes different methods to consistently detect multiple breaks in copula-based dependence measures. Starting with the classical binary segmentation, also the more recent wild binary segmentation (WBS) is considered. For binary segmentation, consistency of the estimators for the location of the breakpoints as well as the number of breaks is proved, taking filtering effects from AR-GARCH models explicitly into account. Monte Carlo simulations based on a factor copula as well as on a Clayton copula model illustrate the strengths and limitations of the procedures. A real data application on recent Euro Stoxx 50 data reveals some interpretable breaks in the dependence structure.