The article explores a type of copulas called hierarchical Archimedean copulas, which can model complex dependency structures. The researchers found that the copula's structure can be uniquely determined from its bivariate margins. They also calculated the distribution of copula values, useful for tests and confidence intervals. The study investigated various aspects of dependence, including orderings and extreme values, with a focus on tail dependencies. Multiple tail dependence indices were derived for these copulas.