We propose a model-independent multivariate sequential procedure to monitor changes in the vector of componentwise unconditional variances in a sequence of p-variate random vectors. The asymptotic behavior of the detector is derived and consistency of the procedure stated. A detailed simulation study illustrates the performance of the procedure confronted with different types of data generating processes. We conclude with an application to the log returns of a group of DAX listed assets.

A multivariate monitoring procedure is presented to detect changes in the parameter vector of the dynamic conditional correlation model proposed by Robert Engle in 2002. The benefit of the proposed procedure is that it can be used to detect changes in both the conditional and unconditional variance as well as in the correlation structure of the model. The detector is based on quasi log likelihood scores. More precisely, standardized derivations of quasi log likelihood contributions of points in the monitoring period are evaluated at parameter estimates calculated from a historical period. The null hypothesis of a constant parameter vector is rejected if these standardized terms differ too much from those that were expected under the assumption of a constant parameter vector. Under appropriate assumptions on moments and the structure of the parameter space, limit results are derived both under null hypothesis and alternatives. In a simulation study, size and power properties of the procedure are examined in various scenarios.