  Modern time series data often exhibit complex dependence and structural
changes which are not easily characterised by shifts in the mean or model
parameters. We propose a nonparametric data segmentation methodology for
multivariate time series termed NP-MOJO. By considering joint characteristic
functions between the time series and its lagged values, NP-MOJO is able to
detect change points in the marginal distribution, but also those in possibly
non-linear serial dependence, all without the need to pre-specify the type of
changes. We show the theoretical consistency of NP-MOJO in estimating the total
number and the locations of the change points, and demonstrate the good
performance of NP-MOJO against a variety of change point scenarios. We further
demonstrate its usefulness in applications to seismology and economic time
series.
