Deep Partial Hedging

Songyan Hou, Thomas Krabichler and Marcus Wunsch

Papers from arXiv.org

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.

Date: 2021-12
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cwa, nep-fmk and nep-rmg
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