Learning to Hedge Swaptions

Battle Count: 79

Highly relevant for institutional quantitative finance desks managing interest rate derivative portfolios. The deep hedging framework provides a practical alternative to traditional rho-hedging for swaption risk management. Particularly relevant for insurance companies, pension funds, and structured product issuers. The findings on two-swap sufficiency and risk-premium harvesting have direct trading implications. However, the focus on European swaptions under a specific parametric model limits immediate applicability to all trading scenarios.

Moderate to high complexity. Requires: (1) DTAFNS model calibration and simulation, (2) KAN pricing network training with Monte Carlo under forward measures, (3) RL agent training with mini-batch SGD and automatic differentiation, (4) Leverage constraint projection, (5) Regularized least-squares for rho-hedging benchmarks. The neural network architectures are relatively simple (4-layer FCNN, 3-layer KAN), but the overall pipeline involves multiple interconnected components. PyTorch is used for training.

Code is available on GitHub (two repositories for deep hedging and KAN pricing). The DTAFNS model parameters are fully specified in Appendix C. Monte Carlo simulation setup is detailed. However, specific random seeds and exact training hyperparameters beyond those stated would need verification. The paper uses 100,000 Monte Carlo paths for training and 100,000 for out-of-sample evaluation.