  Deep hedging (Buehler et al. 2019) is a versatile framework to compute the
optimal hedging strategy of derivatives in incomplete markets. However, this
optimal strategy is hard to train due to action dependence, that is, the
appropriate hedging action at the next step depends on the current action. To
overcome this issue, we leverage the idea of a no-transaction band strategy,
which is an existing technique that gives optimal hedging strategies for
European options and the exponential utility. We theoretically prove that this
strategy is also optimal for a wider class of utilities and derivatives
including exotics. Based on this result, we propose a no-transaction band
network, a neural network architecture that facilitates fast training and
precise evaluation of the optimal hedging strategy. We experimentally
demonstrate that for European and lookback options, our architecture quickly
attains a better hedging strategy in comparison to a standard feed-forward
network.
