  We propose a transformation capable of altering the tail properties of a
distribution, motivated by extreme value theory, which can be used as a layer
in a normalizing flow to approximate multivariate heavy tailed distributions.
We apply this approach to model financial returns, capturing potentially
extreme shocks that arise in such data. The trained models can be used directly
to generate new synthetic sets of potentially extreme returns
