Deep hedging under rough volatility

Blanka Horvath, Josef Teichmann, Žan Žurič

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


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.

Original languageEnglish
Article number138
Issue number7
StatePublished - 2021


  • Deep learning
  • Hedging
  • Rough volatility


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