  This paper proposes different methods to consistently detect multiple breaks
in copula-based dependence measures, mainly focusing on Spearman's $\rho$. The
leading model is a factor copula model due to its usefulness for analyzing data
in high dimensions. Starting with the classical binary segmentation, also the
more recent wild binary segmentation (WBS) and a procedure based on an
information criterion are considered. For all procedures, consistency of the
estimators for the location of the breakpoints as well as the number of breaks
is proved. Monte Carlo simulations indicate that WBS performs best in many, but
not in all, situations. A real data application on recent Euro Stoxx 50 data
reveals the usefulness of the procedures.
