Artikel
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 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.
- Sprache
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Englisch
- Erschienen in
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Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 15 ; Year: 2022 ; Issue: 5 ; Pages: 1-5
- Klassifikation
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Management
- Thema
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machine learning
market frictions
partial hedging
risk management
transaction costs
- Ereignis
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Geistige Schöpfung
- (wer)
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Hou, Songyan
Krabichler, Thomas
Wunsch, Marcus
- Ereignis
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Veröffentlichung
- (wer)
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MDPI
- (wo)
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Basel
- (wann)
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2022
- DOI
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doi:10.3390/jrfm15050223
- Handle
- Letzte Aktualisierung
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10.03.2025, 11:42 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
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Objekttyp
- Artikel
Beteiligte
- Hou, Songyan
- Krabichler, Thomas
- Wunsch, Marcus
- MDPI
Entstanden
- 2022
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