Paper: arXiv 2603.06587

Authors: Minxuan Hu, Ziheng Chen, Jiayu Yi, Wenxi Sun

Abstract

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.

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: The paper provides a strong combination of theoretical formulation (extending QLBS and introducing RLOP) and empirical validation on SPY and XOP option data, specifically addressing downside shortfall under market frictions.

Research Flowchart

  flowchart TD
    A["<b>Research Goal</b><br/>Optimize AI Option Hedging via<br/>Shortfall-Aware RL Frameworks"] --> B["<b>Data & Inputs</b><br/>Listed SPY & XOP Options<br/>Realized Price Paths & Frictions"]
    
    B --> C["<b>RL Methodology</b><br/>Train RLOP & Adaptive QLBS<br/>Penalizing Downside Shortfall"]
    
    C --> D["<b>Evaluation Process</b><br/>Simulate Delta Hedging Paths,<br/>Transaction Costs & Volatility Fit"]
    
    D --> E["<b>Key Outcomes</b><br/>RLOP Reduces Shortfall Frequency<br/>& Improves Tail Risk in Stress"]
    
    D --> F["<b>Practical Insight</b><br/>Parametric Vol-Fit Poorly Predicts<br/>After-Cost Hedging Success"]

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0 of 36 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.