Generative Market Equilibrium Models with Stable Adversarial Learning via Reinforcement
This addresses the challenge of modeling complex financial markets with multiple interacting agents and frictions, offering a computational alternative where traditional methods fall short.
The authors tackled the problem of solving continuous-time financial market equilibria with realistic frictions like trading costs, developing a generative deep reinforcement learning framework with a decoupling feedback system that stabilizes training. Their algorithm successfully learns and provides testable predictions on how asset returns and volatilities emerge from endogenous trading behavior, overcoming limitations of traditional analytical methods.
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.