  A key tool to carry out inference on the unknown copula when modeling a
continuous multivariate distribution is a nonparametric estimator known as the
empirical copula. One popular way of approximating its sampling distribution
consists of using the multiplier bootstrap. The latter is however characterized
by a high implementation cost. Given the rank-based nature of the empirical
copula, the classical empirical bootstrap of Efron does not appear to be a
natural alternative, as it relies on resamples which contain ties. The aim of
this work is to investigate the use of subsampling in the aforementioned
framework. The latter consists of basing the inference on statistic values
computed from subsamples of the initial data. One of its advantages in the
rank-based context under consideration is that the formed subsamples do not
contain ties. Another advantage is its asymptotic validity under minimalistic
conditions. In this work, we show the asymptotic validity of subsampling for
several (weighted, smooth) empirical copula processes both in the case of
serially independent observations and time series. In the former case,
subsampling is observed to be substantially better than the empirical bootstrap
and equivalent, overall, to the multiplier bootstrap in terms of finite-sample
performance.
