  We propose a general framework to construct self-normalized
multiple-change-point tests with time series data. The only building block is a
user-specified one-change-point detecting statistic, which covers a wide class
of popular methods, including cumulative sum process, outlier-robust rank
statistics and order statistics. Neither robust and consistent estimation of
nuisance parameters, selection of bandwidth parameters, nor pre-specification
of the number of change points is required. The finite-sample performance shows
that our proposal is size-accurate, robust against misspecification of the
alternative hypothesis, and more powerful than existing methods. Case studies
of NASDAQ option volume and Shanghai-Hong Kong Stock Connect turnover are
provided.
