| Citation: |
Xiaofeng Yan, Haiyan Wang, Yulian An. FORECASTING SYSTEMIC RISK OF CHINA'S BANKING INDUSTRY BY PARTIAL DIFFERENTIAL EQUATIONS MODEL AND COMPLEX NETWORK[J]. Journal of Applied Analysis & Computation, 2023, 13(6): 3632-3654. doi: 10.11948/20230306
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FORECASTING SYSTEMIC RISK OF CHINA'S BANKING INDUSTRY BY PARTIAL DIFFERENTIAL EQUATIONS MODEL AND COMPLEX NETWORK
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Abstract
The monitoring and controlling of systemic risk have increasingly become the focus of attention in the financial field. It is important and difficult to accurately forecast systemic financial risk. In this paper, we propose a spatio-temporal partial differential equation model to describe the systemic risk of China's Banking Industry based on network, clustering, and real date of 24 China's A-share listed banks. The model considers the combined influence of local risk and transboundary contagion effects, and the prediction relative accuracy is up to 95%. Simulation results confirm that strict joint control measures, the timeliness of central bank intervention, and differences in bank strategies are efficient for reducing systemic risk. To our knowledge, this is the first paper to apply a PDE model to forecast systemic financial risk.
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Keywords:
- Systemic risk /
- forecast /
- complex network /
- partial differential equation /
- joint control
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References
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Xiaofeng Yan, Haiyan Wang, Yulian An. FORECASTING SYSTEMIC RISK OF CHINA'S BANKING INDUSTRY BY PARTIAL DIFFERENTIAL EQUATIONS MODEL AND COMPLEX NETWORK[J]. Journal of Applied Analysis & Computation, 2023, 13(6): 3632-3654. doi: 10.11948/20230306
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Figure 1.
Three clusters embedded in one-dimensional space
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Figure 2.
The directed graph
$ \tilde{G} $ $ G $ -
Figure 3.
Triangle motifs
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Figure 4.
The weighted average of
$ |\Delta \mbox{CoVaR}| $ $ |\Delta\mbox{CoVaR}| $ $ |\Delta \mbox{CoVaR}| $ -
Figure 5.
The weighted average of
$ |\Delta\mbox{CoVaR}| $ $ |\Delta\mbox{CoVaR}| $ $ |\Delta\mbox{CoVaR}| $ -
Figure 6.
The relative accuracy (RA) in Cluster 1, Cluster 2 and Cluster 3 from 2019 to 2022.
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Figure 7.
Comparison of the relative accuracy (RA) among PDE model, BP neural network model and Random Forest model
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Figure 8.
Month-Forecast of weighted
$ |\Delta\mbox{CoVaR}| $ -
Figure 9.
Week-Forecast of weighted
$ |\Delta\mbox{CoVaR}| $ -
Figure 10.
The weighted
$ |\Delta\mbox{CoVaR}| $ -
Figure 11.
The weighted
$ |\Delta\mbox{CoVaR}| $ -
Figure 12.
The
$ |\Delta\mbox{CoVaR}| $ $ \xi=0.3, \phi=0.4 $ -
Figure 13.
The
$ |\Delta\mbox{CoVaR}| $ $ \kappa=0.3, \phi=0.4 $ -
Figure 14.
The
$ |\Delta\mbox{CoVaR}| $ $ \kappa=0.3, \xi=0.3 $