Moving sum procedure for multiple change point detection in large factor models
Methodology
2025-07-24 v2
Abstract
This 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.
Cite
@article{arxiv.2410.02918,
title = {Moving sum procedure for multiple change point detection in large factor models},
author = {Matteo Barigozzi and Haeran Cho and Lorenzo Trapani},
journal= {arXiv preprint arXiv:2410.02918},
year = {2025}
}