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.

中文翻译：

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动态条件相关模型中参数变化的顺序检测

提出了一种多变量监测程序来检测动态条件相关模型的参数向量的变化。该过程可用于检测条件和无条件方差以及模型相关结构的变化。检测器基于个体观察对拟对数似然函数梯度的贡献。更准确地说，在监测期间的时间点准对数似然贡献的标准化导数是根据从历史时期计算的参数估计值来评估的。如果这些标准化项与零相差太大，则拒绝常数参数向量的原假设。临界值是通过参数自举程序获得的。在模拟研究中检查程序的大小和功率特性。最后，用一组资产回报来说明所提出的监控方案的行为。