Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/258222 
Year of Publication: 
2021
Citation: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 9 [Issue:] 7 [Article No.:] 138 [Publisher:] MDPI [Place:] Basel [Year:] 2021 [Pages:] 1-20
Publisher: 
MDPI, Basel
Abstract: 
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.
Subjects: 
deep learning
rough volatility
hedging
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
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