{"id":3718,"date":"2024-01-29T09:19:00","date_gmt":"2024-01-29T09:19:00","guid":{"rendered":"https:\/\/detralytics.com\/?p=3718"},"modified":"2026-08-21T13:31:10","modified_gmt":"2026-08-21T13:31:10","slug":"option-pricing-in-the-heston-model-with-physics-inspired-neural-network","status":"publish","type":"post","link":"https:\/\/detralytics.com\/fr\/publication\/option-pricing-in-the-heston-model-with-physics-inspired-neural-network\/","title":{"rendered":"Option pricing in the heston model with physics inspired neural network"},"content":{"rendered":"
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Option pricing in the heston model with physics inspired neural network<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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DetraNote 2024-1<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Introduction<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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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.\u00a0<\/p>

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.
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Mots-cl\u00e9s : <\/span><\/strong>neural networks, variable annuities, Feynman-Kac equation, life insurance.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Secteur : <\/b>Assurance<\/p>

<\/b>Expertise: <\/b>Assurance Vie<\/p>

Auteurs :<\/b> Donatien Hainaut, Alex Casas<\/p>

\u00c9diteur :<\/b> Detralytics<\/p>

Date:<\/b> January 2024<\/p>

Langue :<\/b> Anglais<\/p>

Pages:<\/b> 21<\/p>

R\u00e9f\u00e9rence :<\/b> Detra Note 2024-1<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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\u00c0 propos des auteurs<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"Donatien\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Donatien Hainaut<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Donatien Hainaut est Conseiller Scientifique chez Detralytics et Professeur \u00e0 l\u2019UCLouvain (Belgique), o\u00f9 il dirige le Master en Data Science \u00e0 orientation statistique. Auparavant, il a occup\u00e9 plusieurs postes acad\u00e9miques, notamment en tant que Professeur Associ\u00e9 \u00e0 la Rennes School of Business et \u00e0 l\u2019ENSAE \u00e0 Paris. Il poss\u00e8de \u00e9galement une solide exp\u00e9rience en entreprise, ayant travaill\u00e9 comme Risk Officer, Quantitative Analyst et ALM Officer.<\/p>

Actuaire qualifi\u00e9 et titulaire d\u2019un doctorat en Asset and Liability Management, ses recherches actuelles portent sur les m\u00e9canismes de contagion dans les processus stochastiques ainsi que sur les applications des r\u00e9seaux de neurones en assurance.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"Alex\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Alex Casas<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t<\/path><\/svg>\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\tAll Publications<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"

This article proposes an alternative to standard pricing methods based on physics-inspired neural networks (PINNs)…<\/p>","protected":false},"author":1,"featured_media":3605,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[133],"tags":[],"class_list":["post-3718","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-publication"],"yoast_head":"\nDetra Note : Option Pricing in the Heston Model (Neural Networks)<\/title>\n<meta name=\"description\" content=\"This actuarial publication proposes an alternative to standard pricing methods based on physics-inspiredneural networks (PINNs).\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/detralytics.com\/fr\/publication\/option-pricing-in-the-heston-model-with-physics-inspired-neural-network\/\" 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