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

Minxuan Hu, Ziheng Chen, Jiayu Yi, Wenxi Sun

TL;DR

Two reinforcement learning frameworks are introduced, 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.

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.

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

TL;DR

Two reinforcement learning frameworks are introduced, 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.

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.
Paper Structure (26 sections, 1 theorem, 8 equations, 10 figures, 6 tables)

This paper contains 26 sections, 1 theorem, 8 equations, 10 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Motivation
  3. Literature review
  4. Replication Pricing and RL
  5. Two RL Formulations for Option Pricing and Hedging
  6. Adaptive-QLBS: Backward Value-Based RL
  7. RLOP: Forward Replication Learning
  8. Neural Policy Training
  9. Empirical Results on Market Data
  10. Market Data and Experimental Slices
  11. Daily option slices and contract universe.
  12. Maturity buckets and moneyness targets.
  13. Day-by-day calibration and delta generation.
  14. Dynamic Hedging Performance
  15. Realized-Path Hedging under Transaction Costs
  16. ...and 11 more sections

Key Result

Proposition 1

For sufficiently large $\epsilon$ that appears in the linear transaction cost assumption $\text{TC}(\Delta u, S)=\epsilon|\Delta u| \,S$, the option price $C(S_0) := -\max_{\pi\in\mathbf{\Pi}} V_0^\pi$ is monotonically increasing in both $\lambda$ and $\epsilon$.

Figures (10)

  • Figure 1: The adaptive-QLBS method takes a backward, value-based approach.
  • Figure 2: The RLOP method takes a forward, replication-based approach.
  • Figure 3: Price under RLOP model (left) and Adaptive-QLBS model (right) given different parameters of volatility. The common setup uses maturity $T=2$ months, strike $K=1$, interest rate $r=4\%$.
  • Figure 4: Price under Adaptive-QLBS model given different levels of hyperparameters: friction $\epsilon$ (left), risk aversion intensity $\lambda$ (middle), and drift $\mu$ (right).
  • Figure 5: Empirical CDFs of after-cost net hedging outcome $\mathrm{PnL}_T^{\mathrm{net}}$ for $\tau=28$d. Columns correspond to SPY 2020Q1, SPY 2025Q2, XOP 2020Q1, and XOP 2025Q2. Top row: ATM ($K/F=1$). Bottom row: mildly out-of-the-money ($K/F=1.03$). Right-shifted curves indicate improved after-cost outcomes; crossings motivate the explicit tail-risk summaries reported in Sec. \ref{['subsubsec:tail_es']}.
  • ...and 5 more figures

Theorems & Definitions (4)

  • Definition 1
  • Proposition 1
  • proof
  • Definition 2