Learning to Hedge Swaptions
This provides a more efficient and resilient hedging method for financial institutions managing swaption portfolios, though it is an incremental application of existing deep hedging techniques to a specific domain.
This paper tackles the problem of dynamically hedging swaptions by applying deep reinforcement learning with three different risk objective functions, finding that using two swaps as hedging instruments achieves near-optimal effectiveness and outperforms traditional rho-hedging even with some model misspecification.
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.