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 effciency 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, variable annuities, Feynman-Kac equation, life insurance.
Sector: Insurance
Expertise: Assurance Vie
Authors: Donatien Hainaut, Alex Casas
Publisher: Detralytics
Date: January 2024
Language: English
Pages: 21
Reference : Detra Note 2024-1
Donatien Hainaut est Conseiller Scientifique chez Detralytics et Professeur à l’UCLouvain (Belgique), où il dirige le Master en Data Science à orientation statistique. Auparavant, il a occupé plusieurs postes académiques, notamment en tant que Professeur Associé à la Rennes School of Business et à l’ENSAE à Paris. Il possède également une solide expérience en entreprise, ayant travaillé comme Risk Officer, Quantitative Analyst et ALM Officer.
Actuaire qualifié et titulaire d’un doctorat en Asset and Liability Management, ses recherches actuelles portent sur les mécanismes de contagion dans les processus stochastiques ainsi que sur les applications des réseaux de neurones en assurance.
Alex is part of the Talent Accelerator Program (TAP) at Detralytics. He developed skills in Life and ALM-related subjects during his first experience with a leading bancassurer. Highly proactive and reliable, he will be able to support you in the various missions related to this field.
