2020
DOI: 10.1155/2020/3690848
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Double-Layer Network Model of Bank-Enterprise Counterparty Credit Risk Contagion

Abstract: Banks and enterprises constitute a multilayered, multiattribute, multicriteria credit-related super network due to financial transaction behaviors, such as credit, wealth management, savings, and derivatives. Such a network has become an important channel for credit risk cross-contagion. This study constructs a two-layer network model of credit risk contagion between the bank and corporate counterparties from the perspective that banks do not withdraw loans from enterprises by considering the influence of corp… Show more

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Cited by 11 publications
(9 citation statements)
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References 66 publications
(84 reference statements)
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“…erefore, common predictive analysis methods cannot truly reflect the nature of the problem. As a remedy, researchers began to study prediction methods based on machine learning models, which have higher superiority compared with traditional prediction methods [21][22][23][24][25]. Common modern machine learning methods include BPNN, K-nearest neighbors (KNN), support vector machine (SVM), and so on.…”
Section: Introductionmentioning
confidence: 99%
“…erefore, common predictive analysis methods cannot truly reflect the nature of the problem. As a remedy, researchers began to study prediction methods based on machine learning models, which have higher superiority compared with traditional prediction methods [21][22][23][24][25]. Common modern machine learning methods include BPNN, K-nearest neighbors (KNN), support vector machine (SVM), and so on.…”
Section: Introductionmentioning
confidence: 99%
“…Gao et al [ 36 ] studied the systemic risk of the multi-layer financial network system under macroeconomic fluctuations and found that firms with medium and high leverage and small asset sizes, as well as banks with smaller asset sizes and fewer bank–firm credit links, are more likely to default. On the assumption that banks do not recover loans from firms, Chen et al [ 38 ] constructed a two-layer credit risk contagion network model between banks and firm counterparties. In multi-layer bank–firm credit networks, they assessed the impact of relevant factors in inter-firm credit-related networks and inter-bank credit-related networks on counterparty credit risk.…”
Section: Introductionmentioning
confidence: 99%
“…Literature [ 23 ] aimed at the random and nonlinear characteristics of traffic flow using LSTM and Gated Recurrent Unit (GRU) neural network methods to predict short-term traffic flow. The experiments had proved that the deep learning methods based on recurrent neural network LSTM and GRU performed much better than the ARIMA models and some other methods [ 25 29 ].…”
Section: Introductionmentioning
confidence: 99%