The Efficacy of “Deep Hedging” vs. Traditional Put-Overlay Strategies in 2025 Market Regimes

Abstract

Classical put-overlays have long been treated as a reliable hedge against tail risk but the market conditions of 2025 expose their limits in ways that theory didn’t fully anticipate. This paper examines where these strategies break down: in markets defined by elevated volatility, wide bid-ask spreads, and structural frictions that quietly erode the protection investors thought they’d bought. Traditional hedging methods, which rely on the systematic purchase of out-of-the-money (OTM) options, are increasingly hampered by high premiums and reliance on static volatility assumptions. During the significant geopolitical disruptions of 2025, most notably the “Tariff Shock” of April, these traditional models proved inadequate, suffering from “premium bleed” and an inability to account for discontinuous market gaps. To address these systemic vulnerabilities, we formulate the hedging process as a stochastic control problem and implement a Deep Hedging framework utilising Deep Reinforcement Learning (DRL). Optimised specifically for Expected Shortfall (ES), the model utilises Long Short-Term Memory (LSTM) layers to process multi-dimensional state vectors, including implied volatility skew and realised turbulence. Our empirical results demonstrate that this AI-optimised policy achieves a 35% reduction in hedging costs while simultaneously improving tail risk protection and draw-down resilience. Notably, the DRL agent exhibits anticipatory behaviour, transitioning from a reactive to a predictive paradigm by adjusting hedge positions prior to observable volatility spikes. These findings suggest that in structurally incomplete markets, optimal risk management has evolved from simple insurance into a process of continuous, regime-aware policy optimisation. This study provides a robust framework for institutional solvency in an era defined by non-linear correlations and rapid liquidity decay.