{"id":4017,"date":"2023-01-24T10:07:27","date_gmt":"2023-01-24T10:07:27","guid":{"rendered":"https:\/\/detralytics.com\/?p=4017"},"modified":"2026-08-21T13:31:30","modified_gmt":"2026-08-21T13:31:30","slug":"risk-management-with-local-least-squares-monte-carlo","status":"publish","type":"post","link":"https:\/\/detralytics.com\/fr\/publication\/risk-management-with-local-least-squares-monte-carlo\/","title":{"rendered":"Risk management with Local Least Squares Monte-Carlo"},"content":{"rendered":"
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Risk management with local least squares monte-carlo<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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DetraNote 2023-1<\/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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\n\t\t\t\t\t\t\t\t\tThe method of least squares Monte-Carlo (LSMC) has become a standard in the insurance and financial sectors for computing the exposure of a company to market risk. The sensitive point of this procedure is the non-linear regression of simulated responses to risk factors. This article proposes a novel approach for this step, based on an apriori segmentation of responses. Using a K-means algorithm, we identify clusters of responses that are next locally regressed on corresponding risk factors. A global function of regression is obtained by combining local models and a logistic regression. The efficiency of the Local Least squares Monte-Carlo (LLSMC) is checked in two illustrations. The first one focuses on butterfly and bull trap options in a Heston stochastic volatility model. The second illustration analyzes the exposure to risks of a participating life insurance.\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>\n

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

Auteurs :<\/b> Adnane Akbaraly,<\/p>\n

Donatien Hainaut<\/p>\n

\u00a0<\/p>\n

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

Date:<\/b> January 2023<\/p>\n

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

Pages:<\/b> 34<\/p>\n

R\u00e9f\u00e9rence :<\/b> Detra Note 2023-1<\/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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Adnane Akbaraly<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Adnane is part of the Talent Accelerator Program (TAP) at Detralytics. Prior to joining Detralytics, Adnane worked at CNP Assurances in retirement department and developed skills in Market Consistency Embedded Value (MCEV) assessment.\u00a0<\/p>

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Having a strong appeal for Data Science applied to actuarial sciences, Adnane has developed technical skills during his experiences in Life and Non-Life. In addition, he had the opportunity to work on an R&D topic that was published in the ASTIN Bulletin.<\/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\"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>

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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\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":4019,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[133],"tags":[],"class_list":["post-4017","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-publication"],"yoast_head":"\nDetra Note : Risk management (Local Least Squares Monte-Carlo)<\/title>\n<meta name=\"description\" content=\"Using a K-means algorithm, we identify clusters of responses that are next locally regressed on corresponding risk factors.\" \/>\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\/risk-management-with-local-least-squares-monte-carlo\/\" \/>\n<meta property=\"og:locale\" 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