In the literature of identifcation through autoregressive conditional heteroscedasticity, Weber (2008) developed the structural constant conditional correlation (SCCC) model. Besides determining linear simultaneous in uences between several variables, this model considers interaction in the structural innovations. Even though this allows for common fundamental driving forces, these cannot explain time variation in correlations of observed variables, which still have to rely on causal transmission e ects. In this context, the present paper extends the analysis to structural dynamic conditional correlation (SDCC). The additional fexibility is shown to make an important contribution in the estimation of empirical real-data examples.