Dynamics Evolution of Credit Risk Contagion in the CRT Market
This work introduces a nonlinear dynamics model of credit risk contagion in the credit risk transfer (CRT) market, which contains time delay, the contagion rate of credit risk, and nonlinear resistance. The model depicts the dynamics behavior characteristics of evolution of credit risk contagion thr...
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Series: | Discrete Dynamics in Nature and Society |
Online Access: | http://dx.doi.org/10.1155/2013/206201 |
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doaj-352f58edb7784967af329795c3fe54e92020-11-24T23:01:35ZengHindawi LimitedDiscrete Dynamics in Nature and Society1026-02261607-887X2013-01-01201310.1155/2013/206201206201Dynamics Evolution of Credit Risk Contagion in the CRT MarketTingqiang Chen0Jianmin He1Qunyao Yin2School of Economics and Management, Southeast University, Nanjing, Jiangsu 211189, ChinaSchool of Economics and Management, Southeast University, Nanjing, Jiangsu 211189, ChinaSchool of Economics and Management, Southeast University, Nanjing, Jiangsu 211189, ChinaThis work introduces a nonlinear dynamics model of credit risk contagion in the credit risk transfer (CRT) market, which contains time delay, the contagion rate of credit risk, and nonlinear resistance. The model depicts the dynamics behavior characteristics of evolution of credit risk contagion through numerical simulation. Meanwhile, numerical simulations show that, in the CRT market, the contagion rate of credit risk and the nonlinear resistance among CRT activities participants have some significant effects on the dynamics behaviors of evolution of credit risk contagion. Specifically, on the one hand, we find that the status curve of credit risk contagion that causes some significant changes with the increase in the contagion rate of credit risk, moreover, emerges a series of Hopf bifurcation and chaotic phenomena in the process of credit risk contagion. On the other hand, Hopf bifurcation and chaotic phenomena appear in advance with the increase in the nonlinear resistance coefficient and time-delay. In addition, there are a series of periodic windows in the chaotic interval inside, including Hopf bifurcation, inverse bifurcation, and chaos.http://dx.doi.org/10.1155/2013/206201 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tingqiang Chen Jianmin He Qunyao Yin |
spellingShingle |
Tingqiang Chen Jianmin He Qunyao Yin Dynamics Evolution of Credit Risk Contagion in the CRT Market Discrete Dynamics in Nature and Society |
author_facet |
Tingqiang Chen Jianmin He Qunyao Yin |
author_sort |
Tingqiang Chen |
title |
Dynamics Evolution of Credit Risk Contagion in the CRT Market |
title_short |
Dynamics Evolution of Credit Risk Contagion in the CRT Market |
title_full |
Dynamics Evolution of Credit Risk Contagion in the CRT Market |
title_fullStr |
Dynamics Evolution of Credit Risk Contagion in the CRT Market |
title_full_unstemmed |
Dynamics Evolution of Credit Risk Contagion in the CRT Market |
title_sort |
dynamics evolution of credit risk contagion in the crt market |
publisher |
Hindawi Limited |
series |
Discrete Dynamics in Nature and Society |
issn |
1026-0226 1607-887X |
publishDate |
2013-01-01 |
description |
This work introduces a nonlinear dynamics model of credit risk contagion in the credit risk transfer (CRT) market, which contains time delay, the contagion rate of credit risk, and nonlinear resistance. The model depicts the dynamics behavior characteristics of evolution of credit risk contagion through numerical simulation. Meanwhile, numerical simulations show that, in the CRT market, the contagion rate of credit risk and the nonlinear resistance among CRT activities participants have some significant effects on the dynamics behaviors of evolution of credit risk contagion. Specifically, on the one hand, we find that the status curve of credit risk contagion that causes some significant changes with the increase in the contagion rate of credit risk, moreover, emerges a series of Hopf bifurcation and chaotic phenomena in the process of credit risk contagion. On the other hand, Hopf bifurcation and chaotic phenomena appear in advance with the increase in the nonlinear resistance coefficient and time-delay. In addition, there are a series of periodic windows in the chaotic interval inside, including Hopf bifurcation, inverse bifurcation, and chaos. |
url |
http://dx.doi.org/10.1155/2013/206201 |
work_keys_str_mv |
AT tingqiangchen dynamicsevolutionofcreditriskcontagioninthecrtmarket AT jianminhe dynamicsevolutionofcreditriskcontagioninthecrtmarket AT qunyaoyin dynamicsevolutionofcreditriskcontagioninthecrtmarket |
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1725639000339775488 |