A structural break test for extremal dependence in β-mixing random vectors

We derive a structural break test for extremal dependence in beta-mixing, possibly high-dimensional random vectors with either asymptotically dependent or asymptotically independent components. Existing tests require serially independent observations with asymptotically dependent components. To avoid estimating a long-run variance, we use self-normalization, which obviates the need to estimate the coefficient of tail dependence when components are asymptotically independent. Simulations show favourable empirical size and power of the test, which we apply to S&P 500 and DAX log-returns. We find evidence for one break in the coefficient of tail dependence for the upper and lower joint tail at the beginning of the 2007-08 financial crisis, leading to more extremal co-movement.