Open access
Date
2021Type
- Journal Article
ETH Bibliography
yes
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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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000499234Publication status
publishedExternal links
Journal / series
RisksVolume
Pages / Article No.
Publisher
MDPISubject
deep learning; rough volatility; hedgingOrganisational unit
03845 - Teichmann, Josef / Teichmann, Josef
Funding
179114 - Mathematical Finance in the light of machine learning (SNF)
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ETH Bibliography
yes
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