Reinforcement Learning Agents Reduce Option Hedging Shortfalls
Minxuan Hu (arXiv preprint submitted Feb 1, 2026) introduces two reinforcement-learning frameworks — Replication Learning of Option Pricing (RLOP) and an adaptive Q-learner in Black‑Scholes (QLBS) — that prioritize shortfall probability and downside-sensitive hedging. Evaluated on listed SPY and XOP options using realized path delta hedging, shortfall probability, and Expected Shortfall, RLOP reduces shortfall frequency across most slices and improves tail risk under stress, while parametric implied-volatility fits often mispredict after-cost hedging performance.
Scoring Rationale
Strong novel RL approach and empirical support, limited by single arXiv preprint status and domain specificity.
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