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Published on: August 7, 2017
Modelling cointegration and Granger causality network to detect long-term equilibrium and diffusion paths in the
Xiangyun Gao1,2,3, Shupei Huang1,2,3, Xiaoqi Sun1,2
1School of Humanities and Economic Management, China University of Geosciences, Beijing 100083, People's Republic of China.
This study introduces network analysis to explore financial systems from a microstructure viewpoint. It reveals how key stock influences spread and identifies industry risk diffusion paths for intervention.
Area of Science:
- Financial econometrics
- Complex network theory
- Systemic risk analysis
Background:
- Macroscopic financial phenomena are rooted in microscopic factors.
- Understanding systemic risk requires analyzing inter-industry relationships.
Purpose of the Study:
- To propose a network analysis paradigm for studying financial systems from a microstructure perspective.
- To identify risk diffusion paths within the real estate industry and its supply chain.
- To provide a method for curbing systemic risk through intervention.
Main Methods:
- Developed cointegration network and Granger causality network models.
- Utilized econometrics and complex network theory.
- Empirically analyzed stock price time series data for the real estate industry and related sectors.
Main Results:
- Identified steady long-term equilibrium relationships using the cointegration network.
- Revealed potential risk diffusion paths via the Granger causality network.
- Demonstrated that influence from a few key stocks can spread rapidly through the system.
Conclusions:
- Network analysis models effectively detect inter-industry diffusion paths and risk transmission.
- Identifying and intervening in transmission mediums can curb systemic risk diffusion.
- This approach enhances understanding of financial system interconnectedness and stability.
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