  We introduce a new goodness-of-fit test for regular vine (R-vine) copula
models, a flexible class of multivariate copulas based on a pair-copula
construction (PCC). The test arises from the information matrix ratio. The
corresponding test statistic is derived and its asymptotic normality is proven.
The test's power is investigated and compared to 14 other goodness-of-fit
tests, adapted from the bivariate copula case, in a high dimensional setting.
The extensive simulation study shows the excellent performance with respect to
size and power as well as the superiority of the information matrix ratio based
test against most other goodness-of-fit tests. The best performing tests are
applied to a portfolio of stock indices and their related volatility indices
validating different R-vine specifications.
