mlsanityData to 14 Oct 2026

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

arXiv 2603.06587 ↗First posted 1 Feb 2026cs.AI q-fin.CP q-fin.RMcited by 0

On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance

Abstract (arXiv)

The deployment of autonomous AI agents in derivatives markets has widened a practical gap between static model calibration and realized hedging outcomes. We introduce two reinforcement learning frameworks, a novel Replication Learning of Option Pricing (RLOP) approach and an adaptive extension of Q-learner in Black-Scholes (QLBS), that prioritize shortfall probability and align learning objectives with downside sensitive hedging. Using listed SPY and XOP options, we evaluate models using realized path delta hedging outcome distributions, shortfall probability, and tail risk measures such as Expected Shortfall. Empirically, RLOP reduces shortfall frequency in most slices and shows the clearest tail-risk improvements in stress, while implied volatility fit often favors parametric models yet poorly predicts after-cost hedging performance. This friction-aware RL framework supports a practical approach to autonomous derivatives risk management as AI-augmented trading systems scale.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 34 references that are in the map. First posted on arXiv 1 Feb 2026.

Builds on

8 of 34 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Deep Reinforcement Learning Algorithms for Option Hedging7 Apr
  2. 2024Deep Hedging with Market Impact20 Feb

Other references · 6

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2018Deep Hedgingcited by 108
  3. 2017QLBS: Q-Learner in the Black-Scholes(-Merton) Worldscited by 23
  4. 2025Does Tone Change the Answer? Evaluating Prompt Politeness Effects on Modern LLMs: GPT, Gemini, and LLaMAcited by 23
  5. 2022Gamma and Vega Hedging Using Deep Distributional Reinforcement Learningcited by 15
  6. 2018The QLBS Q-Learner Goes NuQLear: Fitted Q Iteration, Inverse RL, and Option Portfolioscited by 8

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Reinforcement Learning for Option Hedging: Static Implied-Volatility Fit versus Shortfall-Aware Performance
  2. 2025Learning to Hedge Swaptions
  3. 2025DeltaHedge: A Multi-Agent Framework for Portfolio Options Optimization
  4. 2025Application of Deep Reinforcement Learning to At-the-Money S&P 500 Options Hedging
  5. 2024The Evolution of Reinforcement Learning in Quantitative Finance: A Survey
  6. 2026Trading Confidence: Comprehensive Uncertainty Estimation in Algorithmic Trading
  7. 2025Adaptive Nesterov Accelerated Distributional Deep Hedging for Efficient Volatility Risk Management
  8. 2024Enhancing Deep Hedging of Options with Implied Volatility Surface Feedback Information