Scholarlink

<p>Guaranteed funds provide an important protection vehicle for risk-averse investors during times of increasing market volatility. Protection may be on the capital invested or its return or both: the associated market risk is then transferred onto fund managers and financial institutions, facing, as a result, an optimal hedging problem. In this article we focus on capital protection and address the fund manager replication problem relying on a <i>reinforcement learning</i> (RL) approach. The article clarifies the financial and methodological implications of the RL method and a case study is presented relying on a 1-year guaranteed fund, whose replication by the issuer is tested over the 2018–2024 period. A US fund manager is assumed with a decision space including cash, bond and equity assets, and the optimal replication of the guaranteed fund is formulated considering a telescoping horizon leading to quarterly portfolio revisions. By combining the learning process and the derivation of the optimal policy in a recursive manner, the RL method is able to solve the minimum guarantee fixed-mix problem in minutes, significantly improving classical solution times in stochastic optimization, yet considering a very rich uncertainty model based on a data-driven simulation method and consistent with arbitrage-free conditions in the market.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Guaranteed funds’ replication by reinforcement learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月16日

查看全文
  • Giorgio Consigli,
  • Michael A. H. Dempster,
  • Harvir Hansra,
  • Michael Villaverde

摘要

Guaranteed funds provide an important protection vehicle for risk-averse investors during times of increasing market volatility. Protection may be on the capital invested or its return or both: the associated market risk is then transferred onto fund managers and financial institutions, facing, as a result, an optimal hedging problem. In this article we focus on capital protection and address the fund manager replication problem relying on a reinforcement learning (RL) approach. The article clarifies the financial and methodological implications of the RL method and a case study is presented relying on a 1-year guaranteed fund, whose replication by the issuer is tested over the 2018–2024 period. A US fund manager is assumed with a decision space including cash, bond and equity assets, and the optimal replication of the guaranteed fund is formulated considering a telescoping horizon leading to quarterly portfolio revisions. By combining the learning process and the derivation of the optimal policy in a recursive manner, the RL method is able to solve the minimum guarantee fixed-mix problem in minutes, significantly improving classical solution times in stochastic optimization, yet considering a very rich uncertainty model based on a data-driven simulation method and consistent with arbitrage-free conditions in the market.