Evolutionary Dynamic Optimization-Based Calibration Framework for Agent-Based Financial Market Simulators

Zhenhua Yang1; Muyao Zhong1; Peng Yang1,2

10.1109/CEC60901.2024.10612133

2024-07-05

979-8-3503-0837-2

2024 IEEE Congress on Evolutionary Computation (CEC)

30 June-5 July 2024

Yokohama, Japan

The agent-based financial market simulators serve as an important validation tool for trading strategies. For high-fidelity simulation, it is pivotal to calibrate the parameters of a simulator so that the generated simulation data resembles the observed real market data of interest. In traditional calibration methods, it is typical that the parameters of the simulator are set to be time-invariant. However, the dynamic nature of the real financial market introduces various variability into the behaviors of the market participants over different time intervals, posing in-herent limitations to the traditional methods. A more reasonable approach might involve employing a simulator with time-variant parameters. This suggests that the model parameters can be dynamically adjusted at different stages of the simulation to adapt to the evolving market. Consequently, the calibration problem of the financial market simulators can be treated as a dynamic optimization problem. To dynamically calibrate the simulators, we introduce an Evolutionary Dynamic Optimization (EDO) framework. By monitoring the changes of the best fitness, the whole simulation time interval is adaptively divided into multiple stages. Then the Negatively Correlated Search (NCS) algorithm is employed to effectively adjust the parameters at different simulation stages to better simulate the real financial market. Empirical results on both synthetic and real data verify that our dynamic calibration framework significantly outperforms traditional calibration methods that fixing a parameter for the whole simulation interval. The proposed strategy of detecting dynamic changes is also shown to be more reliable than the naive method of manually segmenting stages. In terms of calibration time, our proposed method significantly improves by nearly 93% compared to the fixed parameter setting, and approximately 61% compared to manual segmentation calibration.

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EI

1.Department of Computer Science and Engineering, Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation, Southern University of Science and Technology, Shenzhen, China
2.Department of Statistics and Data Science, Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China

Zhenhua Yang,Muyao Zhong,Peng Yang. Evolutionary Dynamic Optimization-Based Calibration Framework for Agent-Based Financial Market Simulators[C],2024.