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Erscheinungsjahr: 
2022
Quellenangabe: 
[Journal:] Journal of Risk and Financial Management [ISSN:] 1911-8074 [Volume:] 15 [Issue:] 5 [Article No.:] 223 [Year:] 2022 [Pages:] 1-5
Verlag: 
MDPI, Basel
Zusammenfassung: 
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.
Schlagwörter: 
machine learning
market frictions
partial hedging
risk management
transaction costs
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