Reinforcement Learning-Based Dynamic Hedging under Residual-Stress Calibrated Market Crash Probabilities
Keywords:
reinforcement learning, dynamic hedging, crash probability, residual-stress, systemic risk, financial infrastructure, algorithmic governance, robustnessAbstract
This paper presents a comprehensive systems-level analysis of reinforcement learning-based dynamic hedging strategies that incorporate market crash probabilities calibrated through a residual-stress signal. Traditional hedging approaches, such as delta hedging and variance-optimal methods, rely on volatility estimates that fail to capture the structural fragility latent in financial markets during periods of systemic stress. We propose a framework in which a deep reinforcement learning agent receives, as part of its state space, a residual-stress metric derived from deviations in asset price trajectories from equilibrium paths, thereby enabling the agent to adapt hedging decisions to non-stationary tail risks. The discussion emphasizes the architectural design of the learning system, including state representation, reward shaping, and policy optimization, while also addressing critical trade-offs between hedging accuracy and computational sustainability. We examine the governance implications of deploying such systems at institutional scale, including concerns about model interpretability, fairness across market participants, and the risk of amplifying systemic fragility through collective algorithmic behavior. A case illustration compares the proposed approach with conventional volatility-based hedging in the context of a simulated multi-asset portfolio, highlighting the advantages of stress-calibrated crash probabilities in drawdown protection. Cross-domain comparisons with reinforcement learning applications in power grid management and autonomous vehicle control are drawn to extract generalizable principles for socio-technical infrastructure. The paper concludes with policy recommendations for responsible deployment and calls for future research on hybrid architectures that combine physics-informed signals with data-driven learning.
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