On the Optimal estimating function method for conditional correlation models

chen, yi-ting;

We consider applying the optimal estimating function (OptEF) method
to conditional correlation (CC) models for parameter estimation. This
method is asymptotically more efficient than the Gaussian QML
method under conditional nonnormality. This relative efficiency is
obtained by exploiting the information of the multivariate conditional
third and fourth moments, and is important because conditional
nonnormality is a stylized fact of financial returns.We also demonstrate
how to implement the OptEF method by updating the two-step variant
of the Gaussian QML method under an independence assumption.
This assumption requires that the true model be a constant CC model.
Without this assumption, the updated statistic is still asymptotically
valid, but can only be viewed as an approximation to the OptEF
estimator. By focusing on the constant CC models with different
dimensions, we also conduct a simulation study to confirm our
theoretical results, and provide two empirical examples of dynamic
spot-futures hedging and dynamic variance-minimum portfolios.