Deep Partial Hedging

Songyan Hou, Thomas Krabichler and Marcus Wunsch
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Songyan Hou: Department of Mathematics, ETH Zurich, 8092 Zürich, Switzerland
Thomas Krabichler: Centre for Banking & Finance, Eastern Switzerland University of Applied Sciences, 9001 St. Gallen, Switzerland
Marcus Wunsch: Institute of Wealth & Asset Management, ZHAW Zurich University of Applied Sciences, 8400 Winterthur, Switzerland

JRFM, 2022, vol. 15, issue 5, 1-5

Abstract: Using techniques from deep learning, 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 Föllmer and Leukert. 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. It needs to be noted that, without further modifications, the approach works only if the risk aversion is beyond a certain level.

Keywords: machine learning; market frictions; transaction costs; partial hedging; risk management (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2022
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