对比8种强化学习算法在动态对冲中的表现，发现MCPG最优。
Deep Reinforcement Learning Algorithms for Option Hedging
- 将对冲问题建模为序列决策，用8种DRL算法求解。
- MCPG和PPO在半二次惩罚指标上领先，MCPG超越经典对冲基准。
- 适合金融工程与强化学习交叉研究者阅读。
动态对冲是一种通过定期交易一个或多个金融资产来抵消相关负债风险的金融策略。深度强化学习（DRL）算法通过将该问题视为序列决策问题，已用于寻找最优对冲策略。然而，以往研究通常仅评估一两个DRL算法，难以进行客观比较。本文在动态对冲背景下对比了八种DRL算法：蒙特卡洛策略梯度（MCPG）、近端策略优化（PPO），以及四种深度Q-learning（DQL）变体和两种深度确定性策略梯度（DDPG）变体。其中两种变体首次应用于该任务。实验以Black-Scholes delta对冲为基线，使用GJR-GARCH(1,1)模型生成数据集。结果表明，MCPG在根半二次惩罚指标上表现最佳，紧随其后的是PPO。MCPG是唯一在给定计算预算下超越Black-Scholes基准的算法，可能源于环境中奖励稀疏性的影响。
原文摘要 · Abstract (English)
Dynamic hedging is a financial strategy that consists in periodically transacting one or multiple financial assets to offset the risk associated with a correlated liability. Deep Reinforcement Learning (DRL) algorithms have been used to find optimal solutions to dynamic hedging problems by framing them as sequential decision-making problems. However, most previous work assesses the performance of only one or two DRL algorithms, making an objective comparison across algorithms difficult. In this paper, we compare the performance of eight DRL algorithms in the context of dynamic hedging; Monte Carlo Policy Gradient (MCPG), Proximal Policy Optimization (PPO), along with four variants of Deep Q-Learning (DQL) and two variants of Deep Deterministic Policy Gradient (DDPG). Two of these variants represent a novel application to the task of dynamic hedging. In our experiments, we use the Black-Scholes delta hedge as a baseline and simulate the dataset using a GJR-GARCH(1,1) model. Results show that MCPG, followed by PPO, obtain the best performance in terms of the root semi-quadratic penalty. Moreover, MCPG is the only algorithm to outperform the Black-Scholes delta hedge baseline with the allotted computational budget, possibly due to the sparsity of rewards in our environment.
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