Journal article
Arbitrage-free neural-SDE market models
- Abstract:
- Modelling joint dynamics of liquid vanilla options is crucial for arbitrage-free pricing of illiquid derivatives and managing risks of option trade books. This paper develops a nonparametric model for the European options book respecting underlying financial constraints and while being practically implementable. We derive a state space for prices which are free from static (or model-independent) arbitrage and study the inference problem where a model is learnt from discrete time series data of stock and option prices. We use neural networks as function approximators for the drift and diffusion of the modelled SDE system, and impose constraints on the neural nets such that no-arbitrage conditions are preserved. In particular, we give methods to calibrate neural SDE models which are guaranteed to satisfy a set of linear inequalities. We validate our approach with numerical experiments using data generated from a Heston stochastic local volatility model.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Version of record, pdf, 10.4MB, Terms of use)
-
- Publisher copy:
- 10.1080/1350486X.2023.2257217
Authors
+ Engineering and Physical Sciences Research Council
More from this funder
- Grant:
- EP/L015803/1
- Grant:
- EP/N510129/1
- Publisher:
- Routledge
- Journal:
- Applied Mathematical Finance More from this journal
- Volume:
- 30
- Issue:
- 1
- Pages:
- 1-46
- Publication date:
- 2023-09-28
- Acceptance date:
- 2023-08-28
- DOI:
- EISSN:
-
1466-4313
- ISSN:
-
1350-486X
- Language:
-
English
- Keywords:
- Pubs id:
-
1518143
- Local pid:
-
pubs:1518143
- Deposit date:
-
2023-08-31
- ARK identifier:
Terms of use
- Copyright holder:
- Cohen et al.
- Copyright date:
- 2023
- Rights statement:
- © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
- 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