The group behavior analysis of the high-frequency traders based on Mean Field Games approach

Abstract We present an approach to describe the group behavior of the high-frequency traders in the stock market based on Mean Field Games and optimal control theory. The problem is formalized as a system of coupled PDEs: Kolmogorov–Fokker–Planck, evolving forward in time with initial condition, and Hamilton–Jacobi–Bellman, evolving backwards in time with terminal condition. Under certain assumptions, the system of PDEs can be reduced to Riccati-type ODEs. We solve the inverse problem to describe the behavior of the high-frequency traders during the Chinese stock market crisis in 2015.

Authors

Publication Details

Journal
Journal of Inverse and Ill-Posed Problems
Published
2024-09-16
DOI
https://doi.org/10.1515/jiip-2024-0040
Citations
3
Primary Topic
Stochastic processes and financial applications
Type
article
Field-Weighted Citation Impact
1.62

Funders

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article

The group behavior analysis of the high-frequency traders based on Mean Field Games approach

N. V. Trusov, L. V. Egorov
3 citations
Journal of Inverse and Ill-Posed Problems
Stochastic processes and financial applications
1.62
article

The group behavior analysis of the high-frequency traders based on Mean Field Games approach

N. V. Trusov, L. V. Egorov
article en
3 citations

Abstract

Abstract We present an approach to describe the group behavior of the high-frequency traders in the stock market based on Mean Field Games and optimal control theory. The problem is formalized as a system of coupled PDEs: Kolmogorov–Fokker–Planck, evolving forward in time with initial condition, and Hamilton–Jacobi–Bellman, evolving backwards in time with terminal condition. Under certain assumptions, the system of PDEs can be reduced to Riccati-type ODEs. We solve the inverse problem to describe the behavior of the high-frequency traders during the Chinese stock market crisis in 2015.

Journal of Inverse and Ill-Posed Problems
Russian Science Foundation
Openalex Percentile: Top 15%
Stochastic processes and financial applications
1.62
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