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

• URL: http://arxiv.org/abs/2408.12446v2
• Date: Tue, 27 Aug 2024 14:59:25 GMT
• Title: EX-DRL: Hedging Against Heavy Losses with EXtreme Distributional Reinforcement Learning
• Authors: Parvin Malekzadeh, Zissis Poulos, Jacky Chen, Zeyu Wang, Konstantinos N. Plataniotis,
• Abstract summary: A common approach in Distributional Reinforcement Learning (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)
• Score: 16.972097006411147
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting Framework [77.45983464131977]
  We focus on how likely it is that a RAG model's prediction is incorrect, resulting in uncontrollable risks in real-world applications. Our research identifies two critical latent factors affecting RAG's confidence in its predictions. We develop a counterfactual prompting framework that induces the models to alter these factors and analyzes the effect on their answers.
  arXiv  Detail & Related papers  (2024-09-24T14:52:14Z)
• Policy Gradient Methods for Risk-Sensitive Distributional Reinforcement Learning with Provable Convergence [15.720824593964027]
  Risk-sensitive reinforcement learning (RL) is crucial for maintaining reliable performance in high-stakes applications. This paper introduces a policy gradient method for risk-sensitive DRL with general coherent risk measures. We also design a categorical distributional policy gradient algorithm (CDPG) based on categorical distributional policy evaluation and trajectory gradient estimation.
  arXiv  Detail & Related papers  (2024-05-23T16:16:58Z)
• Data-Adaptive Tradeoffs among Multiple Risks in Distribution-Free Prediction [55.77015419028725]
  We develop methods that permit valid control of risk when threshold and tradeoff parameters are chosen adaptively. Our methodology supports monotone and nearly-monotone risks, but otherwise makes no distributional assumptions.
  arXiv  Detail & Related papers  (2024-03-28T17:28:06Z)
• Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation [54.61816424792866]
  We introduce a general framework on Risk-Sensitive Distributional Reinforcement Learning (RS-DisRL), with static Lipschitz Risk Measures (LRM) and general function approximation. We design two innovative meta-algorithms: textttRS-DisRL-M, a model-based strategy for model-based function approximation, and textttRS-DisRL-V, a model-free approach for general value function approximation.
  arXiv  Detail & Related papers  (2024-02-28T08:43:18Z)
• RiskQ: Risk-sensitive Multi-Agent Reinforcement Learning Value Factorization [49.26510528455664]
  We introduce the Risk-sensitive Individual-Global-Max (RIGM) principle as a generalization of the Individual-Global-Max (IGM) and Distributional IGM (DIGM) principles. We show that RiskQ can obtain promising performance through extensive experiments.
  arXiv  Detail & Related papers  (2023-11-03T07:18:36Z)
• Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory [10.288413564829579]
  A critical aspect of risk awareness involves modeling highly rare risk events (rewards) that could potentially lead to catastrophic outcomes. While risk-aware RL techniques do exist, their level of risk aversion heavily relies on the precision of the state-action value function estimation. Our work proposes to enhance the resilience of RL agents when faced with very rare and risky events by focusing on refining the predictions of the extreme values predicted by the state-action value function distribution.
  arXiv  Detail & Related papers  (2023-08-24T18:23:59Z)
• Policy Evaluation in Distributional LQR [70.63903506291383]
  We provide a closed-form expression of the distribution of the random return. We show that this distribution can be approximated by a finite number of random variables. Using the approximate return distribution, we propose a zeroth-order policy gradient algorithm for risk-averse LQR.
  arXiv  Detail & Related papers  (2023-03-23T20:27:40Z)
• Off-Policy Risk Assessment in Markov Decision Processes [15.225153671736201]
  We develop the first doubly robust (DR) estimator for the CDF of returns in Markov decision processes (MDPs) This estimator enjoys significantly less variance and, when the model is well specified, achieves the Cramer-Rao variance lower bound. We derive the first minimax lower bounds for off-policy CDF and risk estimation, which match our error bounds up to a constant factor.
  arXiv  Detail & Related papers  (2022-09-21T15:40:59Z)
• Probable Domain Generalization via Quantile Risk Minimization [90.15831047587302]
  Domain generalization seeks predictors which perform well on unseen test distributions. We propose a new probabilistic framework for DG where the goal is to learn predictors that perform well with high probability.
  arXiv  Detail & Related papers  (2022-07-20T14:41:09Z)
• Conservative Offline Distributional Reinforcement Learning [34.95001490294207]
  We propose Conservative Offline Distributional Actor Critic (CODAC) for both risk-neutral and risk-averse domains. CODAC adapts distributional RL to the offline setting by penalizing the predicted quantiles of the return for out-of-distribution actions. In experiments, CODAC successfully learns risk-averse policies using offline data collected purely from risk-neutral agents.
  arXiv  Detail & Related papers  (2021-07-12T15:38:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.