Learning to Hedge Swaptions

TL;DR

This paper explores the use of reinforcement learning for dynamic swaption hedging, demonstrating its effectiveness and resilience compared to traditional methods through simulation with a three-factor model.

Contribution

It introduces a deep reinforcement learning framework for swaption hedging, showing improved performance and adaptability over traditional sensitivity-based approaches.

Findings

Deep RL strategies outperform rho-hedging in simulations.

Two swaps are sufficient for near-optimal hedging.

RL-based hedging remains effective under model misspecification.

Abstract

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.