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

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      <subfield code="a">Pérez-Mendoza, Carlos Octavio</subfield>
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      <subfield code="a">Hedging targeted risks with reinforcement learning</subfield>
      <subfield code="b">: application to life insurance contracts with embedded guarantees</subfield>
      <subfield code="c">Carlos Octavio Pérez-Mendoza and Frédéric Godin</subfield>
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      <subfield code="a">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</subfield>
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      <subfield code="g">20/04/2026 Volumen 56 Número 2 - abril 2026 , 27 p.</subfield>
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