ABSTRACT
In this paper, we test for structural changes in the conditional dependence of two-dimensional foreign exchange data. We show that by modeling the conditional dependence structure using copulae, we can detect changes in the dependence beyond linear correlation, such as changes in the tail of the joint distribution. This methodology is relevant for estimating risk-management measures, such as portfolio value-at-risk, pricing multi-name financial instruments, and portfolio asset allocation. Our results include evidence of the existence of changes in the correlation as well as in the fatness of the tail of the dependence between Deutsche mark and Japanese yen.
