Contagion in a network of heterogeneous banks

Gençay, Ramazan; Pang, Hao; Tseng, Michael; Xue, Yi

doi:10.1016/j.jbankfin.2019.105725

Cited by 11 publications

(7 citation statements)

References 27 publications

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“…Otherwise, this information is essential to understand the dynamics of the reciprocal relationships between nodes and, consequently, the dynamics of potential spillover mechanisms. Some authors tried to overcome this problem by employing only publicly available information about the characteristics of every single subject (node), such as information coming from financial statements; based on these data, they tried to infer the structure of the network (Glasserman and Young 2015) and the reaction dynamics after different kinds of shocks, hence highlighting the progression of the contagion (Gençcay et al 2020). In this work, we employ a similar approach based on the information about every node to study the dynamics of systemic risk and contagion in a network represented by the reciprocal relationships between a single bank's customers.…”

Section: Literature Reviewmentioning

confidence: 99%

Dangerous liasons and hot customers for banks

Cerqueti

Pampurini

Pezzola

et al. 2022

Rev Quant Finan Acc

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Understanding the correlation between different customers’ loss of creditworthiness is crucial to credit risk analysis. This paper describes a novel method, based on a weighted network model, in which a set of firms, customers of the same bank, represent the nodes while their links and weights derive from the total transaction amounts. We explore the contagion mechanism deriving from the transmission of the difficulties of one customer to other clients of the same bank so highlighting areas where contagion risk is higher. We use a real proprietary data set provided by a bank to illustrate the proposed approach.

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Section: Literature Reviewmentioning

confidence: 99%

Dangerous liasons and hot customers for banks

Cerqueti

Pampurini

Pezzola

et al. 2022

Rev Quant Finan Acc

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“…In the banking network, as the degree of interbank credit association ε(0 < ε < 1) increases, the probability of credit risk transmission within the banking system and the speed credit risk transmission rate increase. If the bank has a comprehensive risk warning mechanism and adequate capital reserves, then the bank's own risk resistance ω(0 < ω < 1) is strong and is less likely to be affected by credit risk contagion [2,13,56,57]. Second, if the bank's ability to process default information ρ(0 < ρ < 1) is strong, then the ability of the bank to the credit risk screening and risk warning ability is also strong, and the probability of the bank to inhibit the spread of credit risk is great.…”

Section: Credit Risk Contagion Probability Of Bank Networkmentioning

confidence: 99%

Double-Layer Network Model of Bank-Enterprise Counterparty Credit Risk Contagion

et al. 2020

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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 corporate credit defaults on their counterparties under the credit linkage. This study analyzes the mechanism of influencing the evolution of bank-enterprise counterparty credit risk contagion in the two-tier network through theoretical analysis, including the following: the enterprises’ coping ability, risk preference, influence, level of interenterprise credit risk contagion and its network heterogeneity in the interenterprise credit association network, the risk prevention and control ability, business correlation degree, interbank credit risk contagion and its network heterogeneity in the interbank credit association network, the level of credit risk contagion between bank-enterprise counterparty credit association networks, and other factors in the case that banks do not withdraw loans from enterprises. In addition, this study performs a calculation experiment to analyze the characteristics of the evolution of counterparty credit risk contagion of bank and corporate counterparties under the double-layer network. The following four major conclusions can be drawn from the results. First, in the interenterprise credit-related network, the threshold of credit risk contagion rate is positively correlated with the marginal increase in risk perception and risk leveling ability of the enterprise. By contrast, such threshold is negatively correlated with the marginal decrease in the initial economic impact, leverage level, and influence of the enterprise. Moreover, the scale of corporate counterparty credit risk contagion is negatively correlated with the enterprise’s risk perception level and risk spillover ability but positively correlated with the enterprise’s initial economic shock level, the enterprise’s leverage level, and influence. Second, in the interbank credit association network, the threshold of the rate of credit risk contagion is negatively correlated with the marginal decrease in the degree of interbank business association but positively correlated with the marginal increase in the bank’s risk resistance ability and risk information processing ability. Furthermore, the scale of credit risk contagion of bank counterparties is positively correlated with the degree of interbank business association but negatively correlated with the bank’s ability to resist risks and process risk information. Third, if the heterogeneity of the credit-related network of bank-enterprise counterparties is high, then the rate threshold of credit risk contagion is high and the scale of credit risk diffusion is low. Moreover, the scale of credit risk contagion of bank counterparties is positively correlated with the marginal decrease in the degree of corporate and bank counterparties. Finally, the scale of bank counterparty credit risk contagion is a monotonically increasing convex function of the credit risk contagion rate in the enterprise credit association network and among the bank-enterprise networks.

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“…Unlike Fisher, Eboli represented a financial system as a flow network and model the process of direct balancesheet contagion as a flow of losses crossing such a network and focused most of the analysis on the effects that the connectivity and centralization of a financial network have on its exposure to default contagion and then found that both the complete and the star networks show a robust yet fragile response to shocks [10]. Gencay et al discussed that network uncertainty gives rise to an endogenous core-periphery structure which is optimal in mitigating financial contagion yet concentrates systemic risk at the core of big banks [11]. Dastkhan and Gharneh introduced a simulation model to analyze the contagion in financial markets based on the cross shareholding network of firms and the probability and the extent of contagion [12].…”

Section: Literature Overviewmentioning

confidence: 99%

“…Proof. In the stable state, dS k (t)/dt � 0 and dI k (t)/dt � 0, and the solution of equations (6) and (11) in the stable state is obtained as…”

Section: Propositionmentioning

confidence: 99%

See 1 more Smart Citation

Associated Credit Risk Contagion with Incubatory Period: A Network-Based Perspective

Qian

et al. 2020

Complexity

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Associated credit risk is a kind of credit risk among the associated credit entities formed by credit-related entities. Focusing on this hot topic of associated credit risk and the relevant contagion and considering the latent entities and their incubatory period, this paper builds an infectious dynamic model to describe the associated credit risk contagion of associated credit entities based on the mean-field theory of complex networks. Firstly, this paper analyzes the stable state of the associated credit risk contagion in the associated entity network, considering the latent entities and their incubatory period. Secondly, from the perspective of complex network and considering the incubatory period, a SHIS model is built to reveal how the incubatory period influences associated credit risk contagion. Finally, the sensitivity of some parameters is analyzed in the Barabási–Albert (BA) scale-free network. The results show the following: (i) the contagion threshold of associated credit risk is related to the incubatory period of latent entities, the recovery rate and infectivity of infected entities, and the newborn rate of credit entities; (ii) the infectious rate of infected entities, the mortality rate of credit entities, and the important factors stated in (i) are all significantly correlated with the density of infected entities.

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Contagion in a network of heterogeneous banks

Cited by 11 publications

References 27 publications

Dangerous liasons and hot customers for banks

Dangerous liasons and hot customers for banks

Double-Layer Network Model of Bank-Enterprise Counterparty Credit Risk Contagion

Associated Credit Risk Contagion with Incubatory Period: A Network-Based Perspective

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