Spatiotemporal Patterns of Risk Propagation in Complex Financial Networks
The methods of complex networks have been extensively used to characterize information flow in complex systems, such as risk propagation in complex financial networks. However, network dynamics are ignored in most cases despite systems with similar topological structures exhibiting profoundly differ...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/2/1129 |
| _version_ | 1797446443300028416 |
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| author | Tingting Chen Yan Li Xiongfei Jiang Lingjie Shao |
| author_facet | Tingting Chen Yan Li Xiongfei Jiang Lingjie Shao |
| author_sort | Tingting Chen |
| collection | DOAJ |
| description | The methods of complex networks have been extensively used to characterize information flow in complex systems, such as risk propagation in complex financial networks. However, network dynamics are ignored in most cases despite systems with similar topological structures exhibiting profoundly different dynamic behaviors. To observe the spatiotemporal patterns of risk propagation in complex financial networks, we combined a dynamic model with empirical networks. Our analysis revealed that hub nodes play a dominant role in risk propagation across the network and respond rapidly, thus exhibiting a degree-driven effect. The influence of key dynamic parameters, i.e., infection rate and recovery rate, was also investigated. Furthermore, the impacts of two typical characteristics of complex financial systems—the existence of community structures and frequent large fluctuations—on the spatiotemporal patterns of risk propagation were explored. About <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>30</mn><mo>%</mo></mrow></semantics></math></inline-formula> of the total risk propagation flow of each community can be explained by the top <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>10</mn><mo>%</mo></mrow></semantics></math></inline-formula> nodes. Thus, we can control the risk propagation flow of each community by controlling a few influential nodes in the community and, in turn, control the whole network. In extreme market states, hub nodes become more dominant, indicating better risk control. |
| first_indexed | 2024-03-09T13:41:35Z |
| format | Article |
| id | doaj.art-a4163bcb02f9420594fd8a60d8551b3c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T13:41:35Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a4163bcb02f9420594fd8a60d8551b3c2023-11-30T21:06:37ZengMDPI AGApplied Sciences2076-34172023-01-01132112910.3390/app13021129Spatiotemporal Patterns of Risk Propagation in Complex Financial NetworksTingting Chen0Yan Li1Xiongfei Jiang2Lingjie Shao3Department of Finance, Zhejiang University of Finance and Economics, Hangzhou 310018, ChinaDepartment of Finance, Zhejiang Gongshang University, Hangzhou 310018, ChinaCollege of Finance and Information, Ningbo University of Finance and Economics, Ningbo 315175, ChinaDepartment of Finance, Zhejiang University of Finance and Economics, Hangzhou 310018, ChinaThe methods of complex networks have been extensively used to characterize information flow in complex systems, such as risk propagation in complex financial networks. However, network dynamics are ignored in most cases despite systems with similar topological structures exhibiting profoundly different dynamic behaviors. To observe the spatiotemporal patterns of risk propagation in complex financial networks, we combined a dynamic model with empirical networks. Our analysis revealed that hub nodes play a dominant role in risk propagation across the network and respond rapidly, thus exhibiting a degree-driven effect. The influence of key dynamic parameters, i.e., infection rate and recovery rate, was also investigated. Furthermore, the impacts of two typical characteristics of complex financial systems—the existence of community structures and frequent large fluctuations—on the spatiotemporal patterns of risk propagation were explored. About <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>30</mn><mo>%</mo></mrow></semantics></math></inline-formula> of the total risk propagation flow of each community can be explained by the top <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>10</mn><mo>%</mo></mrow></semantics></math></inline-formula> nodes. Thus, we can control the risk propagation flow of each community by controlling a few influential nodes in the community and, in turn, control the whole network. In extreme market states, hub nodes become more dominant, indicating better risk control.https://www.mdpi.com/2076-3417/13/2/1129complex financial systemscomplex financial networkseconophysicsrisk propagationnetwork dynamics |
| spellingShingle | Tingting Chen Yan Li Xiongfei Jiang Lingjie Shao Spatiotemporal Patterns of Risk Propagation in Complex Financial Networks Applied Sciences complex financial systems complex financial networks econophysics risk propagation network dynamics |
| title | Spatiotemporal Patterns of Risk Propagation in Complex Financial Networks |
| title_full | Spatiotemporal Patterns of Risk Propagation in Complex Financial Networks |
| title_fullStr | Spatiotemporal Patterns of Risk Propagation in Complex Financial Networks |
| title_full_unstemmed | Spatiotemporal Patterns of Risk Propagation in Complex Financial Networks |
| title_short | Spatiotemporal Patterns of Risk Propagation in Complex Financial Networks |
| title_sort | spatiotemporal patterns of risk propagation in complex financial networks |
| topic | complex financial systems complex financial networks econophysics risk propagation network dynamics |
| url | https://www.mdpi.com/2076-3417/13/2/1129 |
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