  A novel method is proposed for detecting changes in the covariance structure
of moderate dimensional time series. This non-linear test statistic has a
number of useful properties. Most importantly, it is independent of the
underlying structure of the covariance matrix. We discuss how results from
Random Matrix Theory, can be used to study the behaviour of our test statistic
in a moderate dimensional setting (i.e. the number of variables is comparable
to the length of the data). In particular, we demonstrate that the test
statistic converges point wise to a normal distribution under the null
hypothesis. We evaluate the performance of the proposed approach on a range of
simulated datasets and find that it outperforms a range of alternative recently
proposed methods. Finally, we use our approach to study changes in the amount
of water on the surface of a plot of soil which feeds into model development
for degradation of surface piping.
