  We demonstrate how the uncertainty of parameter point estimates can be
assessed in a maximum likelihood framework in order to prevent overfitting and
erroneous detection of time-inhomogeneity. The class of models we consider are
regular vine (R-vine) copula models, for which we describe a new algorithm for
the exact computation of the score function and observed information. R-vine
copulas constitute a flexible class of dependence models which are constructed
hierarchically from bivariate copulas as building blocks only, and our
algorithm exploits the hierarchical nature for subsequent computation of
log-likelihood derivatives. Results obtained using the proposed methods are
discussed in the context of the asymptotic efficiency of different estimation
methods for R-vine based models. In a substantial application to a dataset of
exchange rates, we obtain clear indications for time-inhomogeneous dependence
between some currency pairs.
