  This work studies the deep learning-based numerical algorithms for optimal
hedging problems in markets with general convex transaction costs on the
trading rates, focusing on their scalability of trading time horizon. Based on
the comparison results of the FBSDE solver by Han, Jentzen, and E (2018) and
the Deep Hedging algorithm by Buehler, Gonon, Teichmann, and Wood (2019), we
propose a Stable Transfer Hedging (ST-Hedging) algorithm, to aggregate the
convenience of the leading-order approximation formulas and the accuracy of the
deep learning-based algorithms. Our ST-Hedging algorithm achieves the same
state-of-the-art performance in short and moderately long time horizon as FBSDE
solver and Deep Hedging, and generalize well to long time horizon when previous
algorithms become suboptimal. With the transfer learning technique, ST-Hedging
drastically reduce the training time, and shows great scalability to
high-dimensional settings. This opens up new possibilities in model-based deep
learning algorithms in economics, finance, and operational research, which
takes advantages of the domain expert knowledge and the accuracy of the
learning-based methods.
