用强化学习构建稳定金融均衡模型，能模拟真实交易摩擦下的资产收益演化。
Generative Market Equilibrium Models with Stable Adversarial Learning via Reinforcement
- 基于对抗学习的生成式强化框架，引入反馈链稳定训练过程
- 在多参与者市场中准确预测资产收益与波动率的内生形成机制
- 适合研究市场微观结构、量化策略或金融建模的学者与从业者
我们提出一种通用计算框架，用于求解在最小建模假设下包含真实金融摩擦（如交易成本）的连续时间金融市场均衡，并支持多个相互作用的参与者。受生成对抗网络启发，该方法采用新型生成式深度强化学习框架，其在对抗训练循环中嵌入解耦反馈系统，称为“强化链接”。该架构通过引入判别器的反馈来稳定训练动态。理论指导的反馈机制实现均衡系统的解耦，克服了传统数值算法面临的挑战。实验表明，该算法不仅能学习，还能对市场参与者内生交易行为如何产生资产收益与波动率提供可检验的预测，而传统分析方法在此方面存在局限。模型设计还具备近似保证。
原文摘要 · Abstract (English)
We present a general computational framework for solving continuous-time financial market equilibria under minimal modeling assumptions while incorporating realistic financial frictions, such as trading costs, and supporting multiple interacting agents. Inspired by generative adversarial networks (GANs), our approach employs a novel generative deep reinforcement learning framework with a decoupling feedback system embedded in the adversarial training loop, which we term as the \emph{reinforcement link}. This architecture stabilizes the training dynamics by incorporating feedback from the discriminator. Our theoretically guided feedback mechanism enables the decoupling of the equilibrium system, overcoming challenges that hinder conventional numerical algorithms. Experimentally, our algorithm not only learns but also provides testable predictions on how asset returns and volatilities emerge from the endogenous trading behavior of market participants, where traditional analytical methods fall short. The design of our model is further supported by an approximation guarantee.
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