The paper proposes a moving sum methodology for detecting multiple change points in high-dimensional time series under a factor model, where changes are attributed to those in loadings as well as emergence or disappearance of factors. We establish the asymptotic null distribution of the proposed test for family-wise error control, and show the consistency of the procedure for multiple change point estimation. Simulation studies and an application to a large dataset of volatilities demonstrate the competitive performance of the proposed method.

翻译：本文提出了一种移动和方法，用于检测高维时间序列在因子模型下的多重变点，其中变化归因于因子载荷的改变以及因子的出现或消失。我们建立了所提检验在控制族系误差下的渐近零分布，并证明了该多重变点估计方法的一致性。模拟研究及对大规模波动率数据集的应用表明，所提方法具有优越的性能。