银行拆借偏好、网络结构演化与金融风险传染

doi:10.16381/j.cnki.issn1003-207x.2024.0135

Abstract

Abstract:

The interbank market plays a crucial role in the modern financial system. It serves as a platform for the exchange of bank liquidity， facilitating interbank lending relationships that enable the seamless flow of funds between banks. However， these lending linkages also expose banks to potential contagion risks. In the event of bank failures， interbank lending relationships can act as conduits for risk transmission， potentially triggering a cascade effect that threatens the stability of the entire banking system. Risk contagion has been examined within bank networks of various structures， as well as the impact of bank behaviors on this contagion. Nevertheless， further research is needed to understand how bank lending preferences influence network structure and evolution， how these preferences affect risk contagion， and how the evolution of interbank lending networks impacts financial risk contagion.To investigate the relationship between bank lending preferences， network evolution， and risk contagion， a dynamic exogenous interbank lending network model is introduced based on banks' lending behaviors and balance sheets. Four foundational hypotheses reflecting the formation and evolution of the actual interbank lending market are proposed. Using these hypotheses， an initial interbank lending market is constructed， the information pertaining is updated to bank assets and liabilities， and the interbank lending network according to established construction rules is developed and revised. Subsequently， the impact of bank lending preferences on the structure and evolution of the bank network is analyzed， as well as on financial contagion within the banking system.Values to the model parameters are assigned based on data from the China Financial Statistical Yearbook， the lending rates in the Chinese interbank market， and relevant literature. Simulation analysis is employed to investigate the relationships between bank lending preferences， bank network structure evolution， and financial risk contagion. The simulation results indicate that：（1） As lending behavior preferences increase， the distribution range of bank node degrees （both in and out） gradually expands， suggesting heightened lending activity in the interbank market. Consequently， the clustering coefficient and efficiency of the network improve， while the average shortest path length decreases. （2） The cumulative distribution of the degree of bank nodes follows a single-stage power law distribution initially but gradually evolves into a two-stage power law distribution over time. （3） The structure of the interbank lending network post-evolution differs from its initial state， with increased divergence in assets and liabilities among banks. For the interval 0<T<20， as lending linkages within the network increase， both aggregation and accessibility are enhanced. When T≥20， the topology of the interbank lending network remains dynamic yet stable. （4） Financial contagion within the interbank market is influenced by bank lending preferences. When a certain threshold is exceeded， an increase in interbank lending linkages can enhance risk-sharing capabilities among banks； however， it also creates additional pathways for risk contagion， potentially amplifying the system's vulnerability and leading to systemic collapse. （5） During the evolution of the interbank lending network， an increase in the interbank lending preference coefficient raises the volatility of the ratio of failed banks， enhances the risk-sharing capacity of the network， and concurrently increases systemic vulnerability.The research presented in this paper contributes to a deeper understanding of the relationship between bank lending preferences， dynamic bank network evolution， and financial risk contagion， thereby enriching the theoretical framework for bank network evolution and financial risk management. Moreover， the findings offer practical guidance for financial regulatory authorities. First， due to the randomness of external shocks in the banking system and the nonlinear characteristics of financial risk contagion， regulators must enhance risk monitoring within the banking sector， improve their ability to diagnose and identify financial risks， and intervene promptly when bank failures occur to prevent the spread of financial risks. Second， regulators should conduct regular stress tests on the banking system to monitor changes in asset quality in real time and adjust relevant regulatory indicators accordingly to bolster the banking system's resilience against risks. Third， in the event of a banking crisis， the central bank should maintain high liquidity levels in the interbank market through policy tools such as open market operations. This approach helps prevent asset prices from declining due to liquidity exhaustion and mitigates the risk of contagion by safeguarding liquidity channels within the banking system.

Key words: interbank market, bank lending preference, network structure evolution, financial risk contagion

CLC Number:

F830.9

Tao Xu,Tingqiang Chen. Bank Lending Preference, Network Structure Evolution and Financial Risk Contagion[J]. Chinese Journal of Management Science, 2026, 34(6): 1-12.

Figures/Tables 9

参数	描述	基准值	取值范围
$N$	银行间市场中初始银行数量	100	-
$τ$	幂律分布参数	1.87	-
$α$	初始权益占银行总资产的比率	0.1	-
$β$	银行间拆入占银行总资产的比率	0.15	-
$γ$	流动性资产占银行总资产的比率	0.05	-
$λ$	银行拆借偏好系数	0.1	[0.01, 10]
$γ k d$	存款波动程度系数	0.04	-
$θ$	资产价格对累计出售资产的敏感性系数	10^-6	-
$y$	外部投资收益率均值	0.06	-
$σ 1$	银行权益的标准差	0.01	-
$σ 2$	外部投资收益率标准差	0.01	-
$r d t$	银行存款基准利率	0.03	-
$r b$	银行拆放基准利率	0.02	-
$w e i g h t$	银行权益对利率的影响权重	0.5	[0.1, 0.9]
$ω$	风险溢价参数	0.01	[0.001, 1]
$ψ$	风险倾向参数	5	[1, 9]

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Bank Lending Preference, Network Structure Evolution and Financial Risk Contagion

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