Modeling and forecasting of dynamically varying covariances have received much attention in the literature. The two most widely used conditional covariances and correlations models are the BEKK and the DCC ones. In this paper, we advance a new method based on Network analysis and a new targeting approach for both the two above models with the aim of better estimate covariance matrices associated with financial time series. Our approach is based on specific groups of highly correlated assets in a financial market, and assuming that these relationships remain unaltered at least in the long run. Based on the estimated parameters, we evaluate our targeting method on simulated series by referring to two well-known loss functions introduced in the literature. Furthermore we find and analyze all the maximal cliques in correlation graphs to evaluate the effectiveness of our method. Results from an empirical case study are encouraging, mainly when the number of assets is not large.