We propose a new test against a change in correlation at an unknown point in time based on cumulated sums of empirical correlations. The test does not require that inputs are independent and identically distributed under the null. We derive its limiting null distribution using a new functional delta method argument, provide a formula for its local power for particular types of structural changes, give some Monte Carlo evidence on its finite-sample behavior, and apply it to recent stock returns.

We propose a specification test for a wide range of parametric models for 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, and an restricted estimate that imposes the structure implied by the model. The procedure is straightforward to implement, is consistent against fixed alternatives, has non-trivial power against local deviations from the null hypothesis of order n −1/2 , and does not require the choice of smoothing parameters. We also provide an empirical application using data on wages in the US.JEL Classification: C12, C14, C31, C52, J31 . Financial support by Deutsche Forschungsgemeinschaft (SFB 823) is gratefully acknowledged. We would like to thank Blaise Melly for helpful comments. The usual disclaimer applies.

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Correlations between random variables play an important role in applications, e.g. in financial analysis. More precisely, accurate estimates of the correlation between financial returns are crucial in portfolio management. In particular, in periods of financial crisis, extreme movements in asset prices are found to be more highly correlated than small movements. It is precisely under these conditions that investors are extremely concerned about changes on correlations. A binary segmentation procedure to detect the number and position of multiple change points in the correlation structure of random variables is proposed. The procedure assumes that expectations and variances are constant and that there are sudden shifts in the correlations. It is shown analytically that the proposed algorithm asymptotically gives the correct number of change points and the change points are consistently estimated.It is also shown by simulation studies and by an empirical application that the algorithm yields reasonable results.

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