  Classical moment based change point tests like the cusum test are very
powerful in case of Gaussian time series with one change point but behave
poorly under heavy tailed distributions and corrupted data. A new class of
robust change point tests based on cusum statistics of robustly transformed
observations is proposed. This framework is quite flexible, depending on the
used transformation one can detect for instance changes in the mean, scale or
dependence of a possibly multivariate time series. Simulations indicate that
this approach is very powerful in detecting changes in the marginal variance of
ARCH processes and outperforms existing proposals for detecting structural
breaks in the dependence structure of heavy tailed multivariate time series.
