用强化学习动态对冲互换期权，效果优于传统方法。
Learning to Hedge Swaptions
- 用RL设计三种目标函数，适应不同风险偏好。
- 用两个互换工具对冲，接近最优效果。
- 在模型有误差时仍优于传统对冲，适合风控需求高者。
本文研究基于强化学习（RL）的深度对冲框架在互换期权动态对冲中的应用，对比其与传统敏感度驱动的rho对冲表现。我们设计了三种不同目标函数（均方误差、下行风险、条件风险价值）的智能体，以捕捉不同风险偏好，并评估这些目标如何影响对冲策略。实验基于三因子无套利动态Nelson-Siegel模型，结果表明：使用两个互换作为对冲工具时，可实现近似最优的对冲效果。深度对冲策略能随市场状态动态调整组合对风险因子的暴露。在存在部分模型误设的情况下，其表现仍持续优于rho对冲策略。结果凸显了强化学习在提升互换期权对冲效率与韧性方面的潜力。
原文摘要 · Abstract (English)
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.
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