Moving Sum Procedure for Multiple Change Point Detection in Large Factor Models

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