  The availability of deep hedging has opened new horizons for solving hedging
problems under a large variety of realistic market conditions. At the same
time, any model - be it a traditional stochastic model or a market generator -
is at best an approximation of market reality, prone to model-misspecification
and estimation errors. This raises the question, how to furnish a modelling
setup with tools that can address the risk of discrepancy between anticipated
distribution and market reality, in an automated way. Automated robustification
is currently attracting increased attention in numerous investment problems,
but it is a delicate task due to its imminent implications on risk management.
Hence, it is beyond doubt that more activity can be anticipated on this topic
to converge towards a consensus on best practices.
  This paper presents a natural extension of the original deep hedging
framework to address uncertainty in the data generating process via an
adversarial approach inspired by GANs to automate robustification in our
hedging objective. This is achieved through an interplay of three modular
components: (i) a (deep) hedging engine, (ii) a data-generating process (that
is model agnostic permitting a large variety of classical models as well as
machine learning-based market generators), and (iii) a notion of distance on
model space to measure deviations between our market prognosis and reality. We
do not restrict the ambiguity set to a region around a reference model, but
instead penalize deviations from the anticipated distribution. Our suggested
choice for each component is motivated by model agnosticism, allowing a
seamless transition between settings. Since all individual components are
already used in practice, we believe that our framework is easily adaptable to
existing functional settings.
