  We propose a novel and unified framework for change-point estimation in
multivariate time series. The proposed method is fully nonparametric, enjoys
effortless tuning and is robust to temporal dependence. One salient and
distinct feature of the proposed method is its versatility, where it allows
change-point detection for a broad class of parameters (such as mean, variance,
correlation and quantile) in a unified fashion. At the core of our method, we
couple the self-normalization (SN) based tests with a novel nested local-window
segmentation algorithm, which seems new in the growing literature of
change-point analysis. Due to the presence of an inconsistent long-run variance
estimator in the SN test, non-standard theoretical arguments are further
developed to derive the consistency and convergence rate of the proposed
SN-based change-point detection method. Extensive numerical experiments and
relevant real data analysis are conducted to illustrate the effectiveness and
broad applicability of our proposed method in comparison with state-of-the-art
approaches in the literature.
