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

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...

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Main Authors: Hui Wang, Runzhe Liu, Yang Zhao, Xiaohui Du
Format: Article
Language:English
Published: Wiley 2021-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2021/5513375
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author Hui Wang
Runzhe Liu
Yang Zhao
Xiaohui Du
author_facet Hui Wang
Runzhe Liu
Yang Zhao
Xiaohui Du
author_sort Hui Wang
collection DOAJ
description 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.
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institution DOAJ
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language English
publishDate 2021-01-01
publisher Wiley
record_format Article
series Complexity
spelling doaj-art-c20d32bc2c804e6eab962cb933a9d0342025-08-20T03:23:30ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/55133755513375Prediction and Application of Computer Simulation in Time-Lagged Financial Risk SystemsHui Wang0Runzhe Liu1Yang Zhao2Xiaohui Du3School of Management, Suzhou University, Suzhou 234000, Anhui, ChinaSchool of Management, Suzhou University, Suzhou 234000, Anhui, ChinaUniversity of Chinese Academy of Sciences, Beijing 100049, ChinaHebei Normal University, Shijiazhuang 050024, Hebei, ChinaBased 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.http://dx.doi.org/10.1155/2021/5513375
spellingShingle Hui Wang
Runzhe Liu
Yang Zhao
Xiaohui Du
Prediction and Application of Computer Simulation in Time-Lagged Financial Risk Systems
Complexity
title Prediction and Application of Computer Simulation in Time-Lagged Financial Risk Systems
title_full Prediction and Application of Computer Simulation in Time-Lagged Financial Risk Systems
title_fullStr Prediction and Application of Computer Simulation in Time-Lagged Financial Risk Systems
title_full_unstemmed Prediction and Application of Computer Simulation in Time-Lagged Financial Risk Systems
title_short Prediction and Application of Computer Simulation in Time-Lagged Financial Risk Systems
title_sort prediction and application of computer simulation in time lagged financial risk systems
url http://dx.doi.org/10.1155/2021/5513375
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AT runzheliu predictionandapplicationofcomputersimulationintimelaggedfinancialrisksystems
AT yangzhao predictionandapplicationofcomputersimulationintimelaggedfinancialrisksystems
AT xiaohuidu predictionandapplicationofcomputersimulationintimelaggedfinancialrisksystems