Deep Hedging under Rough Volatility

Blanka Horvath, Josef Teichmann and Zan Zuric
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Blanka Horvath: ETH Zürich - Department of Mathematics
Josef Teichmann: ETH Zurich; Swiss Finance Institute
Zan Zuric: Imperial College London - Department of Mathematics

No 21-88, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

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 with view to existing theoretical results for those. Furthermore, we suggest parsimonious but suitable network architectures capable of capturing the non-Markoviantity of time-series. Secondly, we analyse the hedging behaviour in these models in terms of P&L distributions and draw comparisons to jump diffusion models if the the rebalancing frequency is realistically small.

Keywords: Imperfect Hedging; Derivatives Pricing; Derivatives Hedging; Deep Learning; Rough Volatility (search for similar items in EconPapers)
JEL-codes: C45 C58 C61 G32 (search for similar items in EconPapers)
Pages: 25 pages
Date: 2021-02
New Economics Papers: this item is included in nep-big, nep-cwa, nep-ore and nep-rmg
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Citations: View citations in EconPapers (20)

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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2188

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