  Functional data analysis (FDA) is a part of modern multivariate statistics
that analyses data providing information about curves, surfaces or anything
else varying over a certain continuum. In economics and empirical finance we
often have to deal with time series of functional data, where we cannot easily
decide, whether they are to be considered as homogeneous or heterogeneous. At
present a discussion on adequate tests of homogenity for functional data is
carried. We propose a novel statistic for detetecting a structural change in
functional time series based on a local Wilcoxon statistic induced by a local
depth function proposed by Paindaveine and Van Bever (2013).
