  In this paper we introduce a deep learning method for pricing and hedging
American-style options. It first computes a candidate optimal stopping policy.
From there it derives a lower bound for the price. Then it calculates an upper
bound, a point estimate and confidence intervals. Finally, it constructs an
approximate dynamic hedging strategy. We test the approach on different
specifications of a Bermudan max-call option. In all cases it produces highly
accurate prices and dynamic hedging strategies with small replication errors.
