  We consider the change-point detection in multivariate continuous and integer
valued time series. We propose a Wald-type statistic based on the estimator
performed by a general contrast function; which can be constructed from the
likelihood, a quasi-likelihood, a least squares method, etc.
  Sufficient conditions are provided to ensure that the statistic convergences
to a well-known distribution under the null hypothesis (of no change) and
diverges to infinity under the alternative; which establishes the consistency
of the procedure. Some examples are detailed to illustrate the scope of
application of the proposed procedure.
  Simulation experiments are conducted to illustrate the asymptotic results.
