Abstract
A multivariate monitoring procedure is presented to detect changes in the parameter vector of the dynamic conditional correlation model. The procedure can be used to detect changes in both the conditional and unconditional variances as well as in the correlation structure of the model. The detector is based on the contributions of individual observations to the gradient of the quasi-log-likelihood function. More precisely, standardized derivatives of quasi-log-likelihood contributions at time 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 zero. Critical values are obtained via a parametric bootstrap-type procedure. Size and power properties of the procedure are examined in a simulation study. Finally, the behavior of the proposed monitoring scheme is illustrated with a group of asset returns.
