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Prediction and Application of Computer Simulation in Time-Lagged Financial Risk Systems

Author

Listed:
  • Hui Wang
  • Runzhe Liu
  • Yang Zhao
  • Xiaohui Du
  • Zhihan Lv

Abstract

Based on the existing financial system risk models, a set of time-lag financial system risk models is established considering the influence brought by time-lag factors on the financial risk system, and the dynamical behavior of this system is analyzed by using chaos theory. Through Matlab simulation, the bifurcation diagram and phase diagram of time-lag risk intensity and control intensity are plotted. The analysis shows that this kind of time-lag financial system risk model has complex dynamic behavior, different motion states will appear when different parameter values are selected, and the time-lag risk intensity parameter also has a very strong influence on the system motion. To ensure the operation of the financial system in a stable state, measures with certain delay effects must be taken to control the risk and to choose the appropriate time-lag control intensity, and too much or too little time-lag control intensity is not conducive to the benign operation of the system.

Suggested Citation

  • Hui Wang & Runzhe Liu & Yang Zhao & Xiaohui Du & Zhihan Lv, 2021. "Prediction and Application of Computer Simulation in Time-Lagged Financial Risk Systems," Complexity, Hindawi, vol. 2021, pages 1-10, April.
    • Handle: RePEc:hin:complx:5513375
    DOI: 10.1155/2021/5513375
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    Cited by:

    1. Zixian Liu & Guansan Du & Shuai Zhou & Haifeng Lu & Han Ji, 2022. "Analysis of Internet Financial Risks Based on Deep Learning and BP Neural Network," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1481-1499, April.

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