Autonomous AI Agents for Option Hedging: Enhancing Financial Stability through Shortfall Aware Reinforcement Learning

A new study explores the deployment of autonomous AI agents in derivatives markets to bridge the gap between static model calibration and realized hedging outcomes. The research introduces two reinforcement learning frameworks: a Replication Learning of Option Pricing (RLOP) approach and an adaptive extension of Q-learner in Black-Scholes (QLBS).

The primary objective is minimizing shortfall probability, aligning learning objectives with downside sensitive hedging. The models were evaluated using listed SPY and XOP options, analyzing realized path delta hedging outcome distributions, shortfall probability, and tail risk measures such as Expected Shortfall.

Empirical results indicate that RLOP reduces shortfall frequency in most slices and shows the clearest tail-risk improvements in stress scenarios. While implied volatility fit often favors parametric models, they poorly predict after-cost hedging performance. This friction-aware RL framework supports a practical approach to autonomous derivatives risk management as AI-augmented trading systems scale.