Deep Partial Hedging
Mathematical Finance
2021-12-15 v1
Abstract
Using techniques from deep learning (cf. [B\"uh+19]), we show that neural networks can be trained successfully to replicate the modified payoff functions that were first derived in the context of partial hedging by [FL00]. Not only does this approach better accommodate the realistic setting of hedging in discrete time, it also allows for the inclusion of transaction costs as well as general market dynamics.
Cite
@article{arxiv.2112.07335,
title = {Deep Partial Hedging},
author = {Songyan Hou and Thomas Krabichler and Marcus Wunsch},
journal= {arXiv preprint arXiv:2112.07335},
year = {2021}
}