Market Simulation

Market simulation uses computational models to recreate financial market dynamics, aiming to understand and predict market behavior under various conditions. Current research focuses on employing agent-based models, often incorporating reinforcement learning algorithms or generative adversarial networks (GANs), to simulate realistic market interactions and stylized facts, including the impact of external shocks. These simulations are valuable for testing trading strategies, evaluating policy interventions (e.g., in electricity markets or interbank networks), and improving the accuracy of financial models, ultimately contributing to better risk management and more informed decision-making.