Modelling cointegration and Granger causality network to detect long-term equilibrium and diffusion paths in the financial system
| Publication Date: |
2018-03-29
|
|---|---|
| Publisher: |
Royal Society
|
| Electronic ISSN: |
2054-5703
|
| Topics: |
Natural Sciences in General
|
| Keywords: |
complexity
|
| Published by: |
| _version_ | 1836398870776512512 |
|---|---|
| autor | Gao, X., Huang, S., Sun, X., Hao, X., An, F. |
| beschreibung | Microscopic factors are the basis of macroscopic phenomena. We proposed a network analysis paradigm to study the macroscopic financial system from a microstructure perspective. We built the cointegration network model and the Granger causality network model based on econometrics and complex network theory and chose stock price time series of the real estate industry and its upstream and downstream industries as empirical sample data. Then, we analysed the cointegration network for understanding the steady long-term equilibrium relationships and analysed the Granger causality network for identifying the diffusion paths of the potential risks in the system. The results showed that the influence from a few key stocks can spread conveniently in the system. The cointegration network and Granger causality network are helpful to detect the diffusion path between the industries. We can also identify and intervene in the transmission medium to curb risk diffusion. |
| citation_standardnr | 6221023 |
| datenlieferant | ipn_articles |
| feed_id | 220702 |
| feed_publisher | Royal Society |
| feed_publisher_url | http://royalsocietypublishing.org/ |
| insertion_date | 2018-03-29 |
| journaleissn | 2054-5703 |
| publikationsjahr_anzeige | 2018 |
| publikationsjahr_facette | 2018 |
| publikationsjahr_intervall | 7984:2015-2019 |
| publikationsjahr_sort | 2018 |
| publisher | Royal Society |
| quelle | Royal Society Open Science |
| relation | http://rsos.royalsocietypublishing.org/cgi/content/short/5/3/172092?rss=1 |
| schlagwort | complexity |
| search_space | articles |
| shingle_author_1 | Gao, X., Huang, S., Sun, X., Hao, X., An, F. |
| shingle_author_2 | Gao, X., Huang, S., Sun, X., Hao, X., An, F. |
| shingle_author_3 | Gao, X., Huang, S., Sun, X., Hao, X., An, F. |
| shingle_author_4 | Gao, X., Huang, S., Sun, X., Hao, X., An, F. |
| shingle_catch_all_1 | Modelling cointegration and Granger causality network to detect long-term equilibrium and diffusion paths in the financial system complexity Microscopic factors are the basis of macroscopic phenomena. We proposed a network analysis paradigm to study the macroscopic financial system from a microstructure perspective. We built the cointegration network model and the Granger causality network model based on econometrics and complex network theory and chose stock price time series of the real estate industry and its upstream and downstream industries as empirical sample data. Then, we analysed the cointegration network for understanding the steady long-term equilibrium relationships and analysed the Granger causality network for identifying the diffusion paths of the potential risks in the system. The results showed that the influence from a few key stocks can spread conveniently in the system. The cointegration network and Granger causality network are helpful to detect the diffusion path between the industries. We can also identify and intervene in the transmission medium to curb risk diffusion. Gao, X., Huang, S., Sun, X., Hao, X., An, F. Royal Society 2054-5703 20545703 |
| shingle_catch_all_2 | Modelling cointegration and Granger causality network to detect long-term equilibrium and diffusion paths in the financial system complexity Microscopic factors are the basis of macroscopic phenomena. We proposed a network analysis paradigm to study the macroscopic financial system from a microstructure perspective. We built the cointegration network model and the Granger causality network model based on econometrics and complex network theory and chose stock price time series of the real estate industry and its upstream and downstream industries as empirical sample data. Then, we analysed the cointegration network for understanding the steady long-term equilibrium relationships and analysed the Granger causality network for identifying the diffusion paths of the potential risks in the system. The results showed that the influence from a few key stocks can spread conveniently in the system. The cointegration network and Granger causality network are helpful to detect the diffusion path between the industries. We can also identify and intervene in the transmission medium to curb risk diffusion. Gao, X., Huang, S., Sun, X., Hao, X., An, F. Royal Society 2054-5703 20545703 |
| shingle_catch_all_3 | Modelling cointegration and Granger causality network to detect long-term equilibrium and diffusion paths in the financial system complexity Microscopic factors are the basis of macroscopic phenomena. We proposed a network analysis paradigm to study the macroscopic financial system from a microstructure perspective. We built the cointegration network model and the Granger causality network model based on econometrics and complex network theory and chose stock price time series of the real estate industry and its upstream and downstream industries as empirical sample data. Then, we analysed the cointegration network for understanding the steady long-term equilibrium relationships and analysed the Granger causality network for identifying the diffusion paths of the potential risks in the system. The results showed that the influence from a few key stocks can spread conveniently in the system. The cointegration network and Granger causality network are helpful to detect the diffusion path between the industries. We can also identify and intervene in the transmission medium to curb risk diffusion. Gao, X., Huang, S., Sun, X., Hao, X., An, F. Royal Society 2054-5703 20545703 |
| shingle_catch_all_4 | Modelling cointegration and Granger causality network to detect long-term equilibrium and diffusion paths in the financial system complexity Microscopic factors are the basis of macroscopic phenomena. We proposed a network analysis paradigm to study the macroscopic financial system from a microstructure perspective. We built the cointegration network model and the Granger causality network model based on econometrics and complex network theory and chose stock price time series of the real estate industry and its upstream and downstream industries as empirical sample data. Then, we analysed the cointegration network for understanding the steady long-term equilibrium relationships and analysed the Granger causality network for identifying the diffusion paths of the potential risks in the system. The results showed that the influence from a few key stocks can spread conveniently in the system. The cointegration network and Granger causality network are helpful to detect the diffusion path between the industries. We can also identify and intervene in the transmission medium to curb risk diffusion. Gao, X., Huang, S., Sun, X., Hao, X., An, F. Royal Society 2054-5703 20545703 |
| shingle_title_1 | Modelling cointegration and Granger causality network to detect long-term equilibrium and diffusion paths in the financial system |
| shingle_title_2 | Modelling cointegration and Granger causality network to detect long-term equilibrium and diffusion paths in the financial system |
| shingle_title_3 | Modelling cointegration and Granger causality network to detect long-term equilibrium and diffusion paths in the financial system |
| shingle_title_4 | Modelling cointegration and Granger causality network to detect long-term equilibrium and diffusion paths in the financial system |
| timestamp | 2025-06-30T23:33:55.972Z |
| titel | Modelling cointegration and Granger causality network to detect long-term equilibrium and diffusion paths in the financial system |
| titel_suche | Modelling cointegration and Granger causality network to detect long-term equilibrium and diffusion paths in the financial system |
| topic | TA-TD |
| uid | ipn_articles_6221023 |