Deep partial hedging

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 i...

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Bibliographische Detailangaben

Veröffentlicht in:	Journal of risk and financial management 2022-01, Vol.15 (5), p.1-5
Hauptverfasser:	Hou, Songyan, Krabichler, Thomas, Wunsch, Marcus
Format:	Artikel
Sprache:	eng
Schlagworte:	Bankruptcy Brownian motion Conflicts of interest Costs Hedging machine learning market frictions Neural networks partial hedging Risk aversion risk management transaction costs Volatility
Online-Zugang:	Volltext
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