We propose a nonparametric procedure to test for changes in correlation matrices at an unknown point in time. The new test requires constant expectations and variances, but only mild assumptions on the serial dependence structure and has considerable power in finite samples. We derive the...

We propose a new set of formal backtests for VaR-forecasts that significantly improve upon existing backtesting procedures. Our new test of unconditional coverage can be used for both one-sided and two-sided testing, which leads to a significantly increased power. Second, we stress the...

The paper proposes a test for constant correlations allowing for breaks at unknown times in the marginal means and variances. Theoretically and in an application to US and German stock returns, we find that not accounting for changes in the marginal moments has severe consequences. This is...

We propose a specification test for a wide range of parametric models for the conditional distribution function of an outcome variable given a vector of covariates. The test is based on the Cramer-von Mises distance between an unrestricted estimate of the joint distribution function of the data,...