  This paper addresses the problem of pricing involved financial derivatives by
means of advanced of deep learning techniques. More precisely, we smartly
combine several sophisticated neural network-based concepts like differential
machine learning, Monte Carlo simulation-like training samples and joint
learning to come up with an efficient numerical solution. The application of
the latter development represents a novelty in the context of computational
finance. We also propose a novel design of interdependent neural networks to
price early-exercise products, in this case, Bermudan swaptions. The
improvements in efficiency and accuracy provided by the here proposed approach
is widely illustrated throughout a range of numerical experiments. Moreover,
this novel methodology can be extended to the pricing of other financial
derivatives.
