  Vine copulas are flexible dependence models using bivariate copulas as
building blocks. If the parameters of the bivariate copulas in the vine copula
depend on covariates, one obtains a conditional vine copula. We propose an
extension for the estimation of continuous conditional vine copulas, where the
parameters of continuous conditional bivariate copulas are estimated
sequentially and separately via gradient-boosting. For this purpose, we link
covariates via generalized linear models (GLMs) to Kendall's $\tau$ correlation
coefficient from which the corresponding copula parameter can be obtained.
Consequently, the gradient-boosting algorithm estimates the copula parameters
providing a natural covariate selection. In a second step, an additional
covariate deselection procedure is applied. The performance of the
gradient-boosted conditional vine copulas is illustrated in a simulation study.
Linear covariate effects in low- and high-dimensional settings are investigated
for the conditional bivariate copulas separately and for conditional vine
copulas. Moreover, the gradient-boosted conditional vine copulas are applied to
the temporal postprocessing of ensemble weather forecasts in a low-dimensional
setting. The results show, that our suggested method is able to outperform the
benchmark methods and identifies temporal correlations better. Eventually, we
provide an R-package called boostCopula for this method.
