  A class of tests for change-point detection designed to be particularly
sensitive to changes in the cross-sectional rank correlation of multivariate
time series is proposed. The derived procedures are based on several
multivariate extensions of Spearman's rho. Two approaches to carry out the
tests are studied: the first one is based on resampling, the second one
consists of estimating the asymptotic null distribution. The asymptotic
validity of both techniques is proved under the null for strongly mixing
observations. A procedure for estimating a key bandwidth parameter involved in
both approaches is proposed, making the derived tests parameter-free. Their
finite-sample behavior is investigated through Monte Carlo experiments.
Practical recommendations are made and an illustration on trivariate financial
data is finally presented.
