Journal article
Deep hedging under rough volatility
- 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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Version of record, pdf, 1.3MB, Terms of use)
-
- Publisher copy:
- 10.3390/risks9070138
Authors
- Publisher:
- MDPI
- Journal:
- Risks More from this journal
- Volume:
- 9
- Issue:
- 7
- Article number:
- 138
- Publication date:
- 2021-07-20
- Acceptance date:
- 2021-06-08
- DOI:
- EISSN:
-
2227-9091
- Language:
-
English
- Keywords:
- Pubs id:
-
1492637
- Local pid:
-
pubs:1492637
- Deposit date:
-
2023-07-23
Terms of use
- Copyright holder:
- Horvath et al.
- Copyright date:
- 2021
- Rights statement:
- © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
- Licence:
- CC Attribution (CC BY)
If you are the owner of this record, you can report an update to it here: Report update to this record