This paper investigates the deep hedging framework, based on reinforcement learning (RL), for the dynamic hedging of swaptions, contrasting its performance with traditional sensitivity-based rho-hedging. We design agents under three distinct objective functions (mean squared error, downside risk, and Conditional Value-at-Risk) to capture alternative risk preferences and evaluate how these objectives shape hedging styles. Relying on a three-factor arbitrage-free dynamic Nelson-Siegel model for our simulation experiments, our findings show that near-optimal hedging effectiveness is achieved when using two swaps as hedging instruments. Deep hedging strategies dynamically adapt the hedging portfolio's exposure to risk factors across states of the market. In our experiments, their out-performance over rho-hedging strategies persists even in the presence some of model misspecification. These results highlight RL's potential to deliver more efficient and resilient swaption hedging strategies.

翻译：本文研究了基于强化学习（RL）的深度对冲框架在利率互换期权动态对冲中的应用，并将其表现与传统基于敏感性的rho对冲方法进行对比。我们在三种不同的目标函数（均方误差、下行风险及条件风险价值）下设计智能体，以捕捉不同的风险偏好，并评估这些目标如何影响对冲风格。通过采用三因子无套利动态Nelson-Siegel模型进行仿真实验，研究发现使用两种互换作为对冲工具时，可实现接近最优的对冲效果。深度对冲策略能根据市场状态动态调整对冲组合对风险因子的暴露。实验表明，即使在存在部分模型设定误差的情况下，其表现仍持续优于rho对冲策略。这些结果突显了强化学习在提供更高效、更具韧性的利率互换期权对冲策略方面的潜力。