mlsanityData to 14 Oct 2026

EX-DRL: Hedging Against Heavy Losses with EXtreme Distributional Reinforcement Learning

arXiv 2408.12446 ↗First posted 22 Aug 2024q-fin.RM cs.LG q-fin.STcited by 0

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

Abstract (arXiv)

Recent advancements in Distributional Reinforcement Learning (DRL) for modeling loss distributions have shown promise in developing hedging strategies in derivatives markets. A common approach in DRL involves learning the quantiles of loss distributions at specified levels using Quantile Regression (QR). This method is particularly effective in option hedging due to its direct quantile-based risk assessment, such as Value at Risk (VaR) and Conditional Value at Risk (CVaR). However, these risk measures depend on the accurate estimation of extreme quantiles in the loss distribution's tail, which can be imprecise in QR-based DRL due to the rarity and extremity of tail data, as highlighted in the literature. To address this issue, we propose EXtreme DRL (EX-DRL), which enhances extreme quantile prediction by modeling the tail of the loss distribution with a Generalized Pareto Distribution (GPD). This method introduces supplementary data to mitigate the scarcity of extreme quantile observations, thereby improving estimation accuracy through QR. Comprehensive experiments on gamma hedging options demonstrate that EX-DRL improves existing QR-based models by providing more precise estimates of extreme quantiles, thereby improving the computation and reliability of risk metrics for complex financial risk management.

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 19 of its 39 references that are in the map. First posted on arXiv 22 Aug 2024.

Builds on

19 of 39 references.

References · 19

  1. 2017A Distributional Perspective on Reinforcement Learningcited by 765
  2. 2017Distributional Reinforcement Learning with Quantile Regressioncited by 474
  3. 2018Implicit Quantile Networks for Distributional Reinforcement Learningcited by 369
  4. 2018Deep Hedgingcited by 108
  5. 2021Conservative Offline Distributional Reinforcement Learningcited by 79
  6. 2021Deep Hedging of Derivatives Using Reinforcement Learningcited by 30
  7. 2017QLBS: Q-Learner in the Black-Scholes(-Merton) Worldscited by 23
  8. 2022Gamma and Vega Hedging Using Deep Distributional Reinforcement Learningcited by 15
  9. 2020Option Hedging with Risk Averse Reinforcement Learningcited by 13
  10. 2022Deep Hedging: Continuous Reinforcement Learning for Hedging of General Portfolios across Multiple Risk Aversionscited by 13
  11. 2023An Alternative to Variance: Gini Deviation for Risk-averse Policy Gradientcited by 12
  12. 2021Understanding the Under-Coverage Bias in Uncertainty Estimationcited by 10
7 more
  1. 2024Hedging Beyond the Mean: A Distributional Reinforcement Learning Perspective for Hedging Portfolios with Structured Productscited by 4
  2. 2021Bias-Corrected Peaks-Over-Threshold Estimation of the CVaRcited by 3
  3. 2019Estimation and uncertainty quantification for extreme quantile regionscited by 2
  4. 2020Equal Risk Pricing and Hedging of Financial Derivatives with Convex Risk Measurescited by 2
  5. 2024A Robust Quantile Huber Loss With Interpretable Parameter Adjustment In Distributional Reinforcement Learningcited by 2
  6. 2024A unified uncertainty-aware exploration: Combining epistemic and aleatory uncertaintycited by 2
  7. 2024Catastrophic-risk-aware reinforcement learning with extreme-value-theory-based policy gradientscited by 2

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  2. 2025Distributional Reinforcement Learning on Path-dependent Options
  3. 2025Risk-averse policies for natural gas futures trading using distributional reinforcement learning
  4. 2018The QLBS Q-Learner Goes NuQLear: Fitted Q Iteration, Inverse RL, and Option Portfolios
  5. 2024Optimizing Deep Reinforcement Learning for American Put Option Hedging
  6. 2024Deep Hedging with Market Impact
  7. 2025Tail-Safe Hedging: Explainable Risk-Sensitive Reinforcement Learning with a White-Box CBF--QP Safety Layer in Arbitrage-Free Markets
  8. 2024Hedging American Put Options with Deep Reinforcement Learning