Option pricing in the Heston model with physics inspired neural networks

Donatien Hainaut () and Alex Casas ()
Additional contact information
Donatien Hainaut: UCLouvain- LIDAM
Alex Casas: Detralytics

Annals of Finance, 2024, vol. 20, issue 3, No 4, 353-376

Abstract: Abstract In absence of a closed form expression such as in the Heston model, the option pricing is computationally intensive when calibrating a model to market quotes. this article proposes an alternative to standard pricing methods based on physics-inspired neural networks (PINNs). A PINN integrates principles from physics into its learning process to enhance its efficiency in solving complex problems. In this article, the driving principle is the Feynman-Kac (FK) equation, which is a partial differential equation (PDE) governing the derivative price in the Heston model. We focus on the valuation of European options and show that PINNs constitute an efficient alternative for pricing options with various specifications and parameters without the need for retraining.

Keywords: Neural networks; Options; Heston model; Feynman-Kac equation (search for similar items in EconPapers)
JEL-codes: C6 G1 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10436-024-00452-7 Abstract (text/html)
Access to full text is restricted to subscribers.

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:kap:annfin:v:20:y:2024:i:3:d:10.1007_s10436-024-00452-7

Ordering information: This journal article can be ordered from
http://www.springer.com/finance/journal/10436/PS2

DOI: 10.1007/s10436-024-00452-7

Access Statistics for this article

Annals of Finance is currently edited by Anne Villamil

More articles in Annals of Finance from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().