  For the problem of estimating lower tail and upper tail copulas, we propose
two bootstrap procedures for approximating the distribution of the
corresponding empirical tail copulas. The first method uses a multiplier
bootstrap of the empirical tail copula process and requires estimation of the
partial derivatives of the tail copula. The second method avoids this
estimation problem and uses multipliers in the two-dimensional empirical
distribution function and in the estimates of the marginal distributions. For
both multiplier bootstrap procedures, we prove consistency. For these
investigations, we demonstrate that the common assumption of the existence of
continuous partial derivatives in the the literature on tail copula estimation
is so restrictive, such that the tail copula corresponding to tail independence
is the only tail copula with this property. Moreover, we are able to solve this
problem and prove weak convergence of the empirical tail copula process under
nonrestrictive smoothness assumptions that are satisfied for many commonly used
models. These results are applied in several statistical problems, including
minimum distance estimation and goodness-of-fit testing.
