Paper: arXiv 2512.06639

Authors: Zaniar Ahmadi, Frédéric Godin

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.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: This paper successfully extends deep reinforcement learning dynamic hedging to interest rate swaptions using a three-factor discrete-time dynamic Nelson-Siegel model. It features solid mathematical foundations, clear empirical comparative analysis across multiple risk objectives, and public code repositories for strong reproducibility.

Research Flowchart

  flowchart TD
    A["Research Goal: Evaluate Deep Hedging (RL) vs. Traditional Rho-Hedging for Swaptions"] --> B["Simulation & Data: 3-Factor Arbitrage-Free Dynamic Nelson-Siegel Model"]
    B --> C["Agent Training: RL Deep Hedging with 3 Risk Objectives (MSE, Downside Risk, CVaR)"]
    C --> D["Execution & Benchmarking: Dynamic Portfolio Adjustment using 2 Hedging Swaps vs. Rho-Hedging"]
    D --> E["Stress Testing: Performance Evaluation under Model Misspecification & Market Shifts"]
    E --> F["Key Outcomes: RL achieves near-optimal efficiency, dynamically adapts risk exposure, and outperforms rho-hedging"]

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3 of 48 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.