Deep Calibration of Market Simulations using Neural Density Estimators and Embedding Networks
This addresses the open problem of realistic financial exchange simulation for researchers and practitioners, offering an incremental improvement over existing agent-based models.
The paper tackles the challenge of calibrating market simulators to specific trading periods by developing a novel approach using neural density estimators and embedding networks, demonstrating its ability to correctly identify high-probability parameter sets on synthetic and historical data without manual feature selection.
The ability to construct a realistic simulator of financial exchanges, including reproducing the dynamics of the limit order book, can give insight into many counterfactual scenarios, such as a flash crash, a margin call, or changes in macroeconomic outlook. In recent years, agent-based models have been developed that reproduce many features of an exchange, as summarised by a set of stylised facts and statistics. However, the ability to calibrate simulators to a specific period of trading remains an open challenge. In this work, we develop a novel approach to the calibration of market simulators by leveraging recent advances in deep learning, specifically using neural density estimators and embedding networks. We demonstrate that our approach is able to correctly identify high probability parameter sets, both when applied to synthetic and historical data, and without reliance on manually selected or weighted ensembles of stylised facts.