Artikel
Pricing and hedging American-style options with deep learning
In this paper we introduce a deep learning method for pricing and hedging American-style options. It first computes a candidate optimal stopping policy. From there it derives a lower bound for the price. Then it calculates an upper bound, a point estimate and confidence intervals. Finally, it constructs an approximate dynamic hedging strategy. We test the approach on different specifications of a Bermudan max-call option. In all cases it produces highly accurate prices and dynamic hedging strategies with small replication errors.
- Language
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Englisch
- Bibliographic citation
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Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 13 ; Year: 2020 ; Issue: 7 ; Pages: 1-12 ; Basel: MDPI
- Classification
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Wirtschaft
- Subject
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American option
Bermudan option
deep neural network
hedging strategy
lower bound
optimal stopping
upper bound
- Event
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Geistige Schöpfung
- (who)
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Becker, Sebastian
Cheridito, Patrick
Jentzen, Arnulf
- Event
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Veröffentlichung
- (who)
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MDPI
- (where)
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Basel
- (when)
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2020
- DOI
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doi:10.3390/jrfm13070158
- Handle
- Last update
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10.03.2025, 11:43 AM CET
Data provider
This object is provided by:
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Becker, Sebastian
- Cheridito, Patrick
- Jentzen, Arnulf
- MDPI
Time of origin
- 2020
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