  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 BEKK and DCC. In this paper, we advance
a new method to introduce targeting in both models to estimate matrices
associated with financial time series. Our approach is based on specific groups
of highly correlated assets in a financial market, and these relationships
remain unaltered over time. 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 and Network analysis. We find 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.
