Deep Hedging under Rough Volatility

Blanka Horvath, Josef Teichmann and Žan Žurič
Additional contact information
Blanka Horvath: Technische Universität München, King’s College London and The Alan Turing Institute, London WC2R 2LS, UK
Josef Teichmann: ETH Zürich, 8092 Zürich, Switzerland
Žan Žurič: Faculty of Natural Science, Imperial College London, London SW7 2AZ, UK

Risks, 2021, vol. 9, issue 7, 1-20

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.

Keywords: deep learning; rough volatility; hedging (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
https://www.mdpi.com/2227-9091/9/7/138/pdf (application/pdf)
https://www.mdpi.com/2227-9091/9/7/138/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jrisks:v:9:y:2021:i:7:p:138-:d:597662

Access Statistics for this article

Risks is currently edited by Mr. Claude Zhang

More articles in Risks from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().