  Considering multivariate strongly mixing time series, nonparametric tests for
a constant copula with specified or unspecified change point (candidate) are
derived; the tests are consistent against general alternatives. A tapered block
multiplier technique based on serially dependent multiplier random variables is
provided to estimate p-values of the test statistics. Size and power of the
tests in finite samples are evaluated with Monte Carlo simulations. The block
multiplier technique might have several other applications for statistical
inference on copulas of serially dependent data.
