Artikel
Deep hedging under rough volatility
We investigate the performance of the Deep Hedging framework under training paths beyond the (finite dimensional) Markovian setup. In particular, we analyse the hedging performance of the original architecture under rough volatility models in view of existing theoretical results for those. Furthermore, we suggest parsimonious but suitable network architectures capable of capturing the non-Markoviantity of time-series. We also analyse the hedging behaviour in these models in terms of Profit and Loss (P&L) distributions and draw comparisons to jump diffusion models if the rebalancing frequency is realistically small.
- Language
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Englisch
- Bibliographic citation
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Journal: Risks ; ISSN: 2227-9091 ; Volume: 9 ; Year: 2021 ; Issue: 7 ; Pages: 1-20 ; Basel: MDPI
- Classification
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Wirtschaft
- Subject
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deep learning
rough volatility
hedging
- Event
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Geistige Schöpfung
- (who)
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Horvath, Blanka Nora
Teichmann, Josef
Žuric̆, Žan
- Event
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Veröffentlichung
- (who)
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MDPI
- (where)
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Basel
- (when)
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2021
- DOI
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doi:10.3390/risks9070138
- Handle
- Last update
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10.03.2025, 11:42 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
- Horvath, Blanka Nora
- Teichmann, Josef
- Žuric̆, Žan
- MDPI
Time of origin
- 2021
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