  We study pricing and hedging under parameter uncertainty for a class of
Markov processes which we call generalized affine processes and which includes
the Black-Scholes model as well as the constant elasticity of variance (CEV)
model as special cases. Based on a general dynamic programming principle, we
are able to link the associated nonlinear expectation to a variational form of
the Kolmogorov equation which opens the door for fast numerical pricing in the
robust framework.
  The main novelty of the paper is that we propose a deep hedging approach
which efficiently solves the hedging problem under parameter uncertainty. We
numerically evaluate this method on simulated and real data and show that the
robust deep hedging outperforms existing hedging approaches, in particular in
highly volatile periods.
