Hedging targeted risks with reinforcement learning : application to life insurance contracts with embedded guarantees

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<dc:creator>Pérez-Mendoza, Carlos Octavio</dc:creator>
<dc:creator>Godin, Fréderic</dc:creator>
<dc:creator>International Actuarial Association</dc:creator>
<dc:date>2026-04-20</dc:date>
<dc:description xml:lang="es">Sumario: The article presents a risk hedging framework based on deep reinforcement learning applied to life insurance contracts with embedded financial guarantees, such as variable annuities. The methodology relies on Shapley value decompositions to attribute the contributions of different risk sources to the contract's cash flows. Building on this decomposition, a targeted hedging strategy is developed that allows only selected risks to be mitigated while preserving exposure to the remaining ones. The model further incorporates a local risk penalization based on Conditional Value-at-Risk (CVaR) to enhance hedging stability. Numerical results demonstrate a significant improvement over traditional methods such as delta hedging and standard deep hedging</dc:description>
<dc:identifier>https://documentacion.fundacionmapfre.org/documentacion/publico/es/bib/190610.do</dc:identifier>
<dc:language>eng</dc:language>
<dc:rights xml:lang="es">InC - http://rightsstatements.org/vocab/InC/1.0/</dc:rights>
<dc:subject xml:lang="es">Cálculo actuarial</dc:subject>
<dc:subject xml:lang="es">Seguros de vida riesgo</dc:subject>
<dc:subject xml:lang="es">Gerencia de riesgos</dc:subject>
<dc:subject xml:lang="es">Renta vitalicia</dc:subject>
<dc:subject xml:lang="es">Inteligencia artificial</dc:subject>
<dc:subject xml:lang="es">Simulación Monte Carlo</dc:subject>
<dc:type xml:lang="es">Artículos y capítulos</dc:type>
<dc:title xml:lang="es">Hedging targeted risks with reinforcement learning : application to life insurance contracts with embedded guarantees</dc:title>
<dc:relation xml:lang="es">En: Astin bulletin. - Belgium : ASTIN and AFIR Sections of the International Actuarial Association = ISSN 0515-0361. - 20/04/2026 Volumen 56 Número 2 - abril 2026 , 27 p.</dc:relation>
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