EX-DRL:利用极端分布强化学习抵御严重损失

Parvin Malekzadeh, Zissis Poulos, Jacky Chen, Zeyu Wang, Konstantinos N. Plataniotis
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引用次数: 0

摘要

分布强化学习(DRL)在损失分布建模方面的最新进展,为衍生品市场对冲策略的开发带来了希望。DRL 中的一种常见方法是利用定量回归(QR)学习指定水平上损失分布的定量。这种方法在期权对冲中尤为有效,因为它可以直接进行基于量值的风险评估,如风险值(VaR)和条件风险值(CVaR)。然而,这些风险度量依赖于对损失分布尾部极端量值的精确估计,由于尾部数据的稀缺性和极端性,基于 QR 的 DRL 难以精确估计尾部数据。为了解决这个问题,我们提出了 EXtreme DRL(EX-DRL),它通过使用广义帕累托分布(GPD)对损失分布的尾部进行建模,从而增强了极端量值预测。该方法引入了补充数据,以缓解极端量级观测数据稀缺的问题,从而通过 QR 提高了估计精度。对伽马对冲期权的综合实验表明,EX-DRL 改进了现有的基于 QR 的模型,提供了更精确的极端量值估计,从而改进了用于综合金融风险管理的风险度量的计算和可靠性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
EX-DRL: Hedging Against Heavy Losses with EXtreme Distributional Reinforcement Learning
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.
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