Interbank Lending Networks Research Articles

Risk contagion in interbank lending networks: A multi-agent-based modeling and simulation perspective

The interbank lending network, while facilitating transactions, poses risk contagion in the banking system. To address challenges in existing research, this paper models the interbank lending network using a multi-agent approach and generates a dynamic network through a bidirectional matching mechanism(BiMM). Additionally, We divide banks into three categories based on realistic bank balance sheet data. Given the fact that banks of different categories exhibit different degrees of contagion, we propose the weighted DebtRank (W-DebtRank). Then, comprehensive simulation analysis is conducted on the risk contagion of heterogeneous banks in the dynamic network using W-DebtRank. The proposed method addresses four issues: the heterogeneity of banks, the dynamic nature of networks, the practicality of lending strategies, and the comprehensiveness of risk analysis. The model is validated using the SRISK metric and the Monte Carlo-based SYMBOL simulation model. Through multiple perspectives in simulation experiments, the risk contagion process among banks is deeply investigated in our paper.

Systemic risk in Chinese interbank lending networks: insights from short-term and long-term lending data

Systemic risk in Chinese interbank lending networks: insights from short-term and long-term lending data

Financial risk contagion based on dynamic multi-layer network between banks and firms

Banks and firms play pivotal roles within the financial system and serve as primary channels for systemic risk contagion. While progress has been made in understanding risk contagion within financial network systems, many studies tend to overlook the intricate interplay of multi-channel risk transmission and the dynamic evolution of network structures. Leveraging complex network theory, we have developed a dynamic multi-layer financial network risk transmission model that encompasses traditional bank–firm lending relationships and incorporates dynamic short-term loans. Through our analysis of various parameters such as loan recovery ratios, cross-shareholding ratios, network structures, short-term loan ratios, and attack strategies, we have made significant observations: In scenarios where both the interbank lending network and cross-shareholding network among firms exhibit random network structure, the financial network presents the highest level of risk. Excessive deviations in network average degrees, either too high or too low, can also heighten network vulnerability. An increase in the recovery ratio of interbank loans and the cross-shareholding ratio among firms corresponds to an escalation in systemic risk. Nodes with higher lending assets, more liquid assets, greater total assets, or higher out-degrees within the network become significant contributors to an elevated network bankruptcy rate when subjected to targeted attacks. Notably, the dynamic short-term loan network significantly amplifies systemic risks, with this amplification effect intensifying as the scale of short-term loans increases. From both macro and micro perspectives, our findings furnish regulatory authorities with novel analytical tools and insights to effectively mitigate and manage systemic risks in the financial sector.

Do network characteristics affect systemic risk? Evidence from the European banking system

ABSTRACT Systemic risk is one of the main concerns for banks charged with maintaining overall financial stability. This paper adopts the minimum density method to construct an interbank lending network for European banks and examines how the network structure affects systemic risk. The result reveals that banks positioned at the core of the network exhibit higher levels of systemic risk. Moreover, we find that banks with higher network centrality can show larger systemic risk during times of distress. We demonstrate the robustness of our results by addressing potential endogeneity in the model, replacing network characteristics, and reconstructing the interbank network.

HFTCRNet: Hierarchical Fusion Transformer for Interbank Credit Rating and Risk Assessment.

As a prominent application of deep neural networks in financial literature, bank credit ratings play a pivotal role in safeguarding global economic stability and preventing crises. In the contemporary financial system, interconnectivity among banks has reached unprecedented levels. However, many existing credit risk models continue to assess each bank independently, resulting in inevitable suboptimal performance. Thus, developing advanced neural networks to model intricate temporal dynamics and interconnected relationships in the banking system is essential for an effective credit rating and risk assessment learning system. To this end, we propose a novel hierarchical fusion transformer for interbank credit rating and risk assessment (HFTCRNet), which includes the long-term temporal transformer (LT 3 ) module, short-term cross-graph transformer (STCGT) module, attentive risk contagion transformer (ARCT) module, and hierarchical fusion transformer (HFT) module to capture the long-term growth trajectories of banks, the short-term interbank network variance, the potential propagation of risks within interbank network, and integrate these information hierarchically. We further develop an interbank credit rating dataset, encompassing quarterly financial data, interbank lending networks, and key indicators such as credit ratings and systemic risk (SRISK) for 4548 banks from 2016Q1 to 2023Q1. Notably, we also adapt the minimum density algorithm to stabilize the interbank loan network over time, aiding in the analysis of long-term and short-term network effects. Our learning system uses semi-supervised learning to handle labels of varying sparsity, integrating credit ratings and SRISK for a comprehensive assessment of individual bank creditworthiness and systemic interbank risk. Extensive experimental results on our interbank dataset show that HFTCRNet not only outperforms all the baselines in terms of credit rating accuracy but also can evaluate the systemic risk within the interbank network. Code will be available at: https://github.com/AI4Risk/HFTCRNet.

Interpersonal versus interbank lending networks: The role of intermediation in risk-sharing

Analyzing the interpersonal lending network of a Hungarian village in a disadvantaged region, we find strong intermediary activity and a tiered core-periphery structure. We show that the main motive behind lending is not altruism or profit-seeking, but risk-sharing which is the most accentuated in poor-to-poor and Roma-to-Roma relations. Comparing this informal lending market to a formal interbank market, we find more similarities than differences. In both markets, intermediation is a key element in risk-sharing and an effective tool to cope with liquidity risk. Regulatory and development policies should respect the existing institutions of risk-sharing.

Formation of Optimal Interbank Lending Networks under Liquidity Shocks

Formation of Optimal Interbank Lending Networks under Liquidity Shocks

Monitoring sparse and attributed networks with online Hurdle models

In this article we create a novel monitoring system to detect changes within a sequence of networks. Specifically, we consider sparse, weighted, directed, and attributed networks. Our approach uses the Hurdle model to capture sparsity and explain the weights of the edges as a function of the node and edge attributes. Here, the weight of an edge represents the number of interactions between two nodes. We then integrate the Hurdle model with a state-space model to capture temporal dynamics of the edge formation process. Estimation is performed using an extended Kalman Filter. Statistical process control charts are used to monitor the network sequence in real time in order to identify changes in connectivity patterns that are caused by regime shifts. We show that the proposed methodology outperforms alternative approaches on both synthetic and real data. We also perform a detailed case study on the 2007–2009 financial crisis. Demonstrating the promise of the proposed approach as an early warning system, we show that our method applied to financial interbank lending networks would have raised alarms to the public prior to key events and announcements by the European Central Bank.

A research on driver nodes identification in Chinese interbank networks: based on the controllability theory of complex network

ABSTRACT We propose a method of network controllability to identify the driver nodes in Chinese interbank lending networks from 2008 to 2014, and analyse the role and influence of the driver nodes in the banking system. The results show that it is enough to control only nD (the fraction of minimum driver nodes) of the banks to steer the entire banking system. We find that the driver nodes are often not the most closely connected banks, nor the largest banks. We further introduce the concept of control centrality to quantify the ability of a single node to control a banking network, and we validate our results by showing that the driver nodes have larger control centrality. Our conclusions can help the regulatory authorities determine the institutions that should be supervised or controlled, and provide new ideas and theoretical basis for improving the macroprudential supervision policy.

Change Detection in Core-Periphery Networks: A Case Study on Detecting Financial Crises in the Interbank Market

We develop and present a new methodology to detect regime changes within a sequence of sparse networks that have overlapping and evolving community structure. The core of the methodology is a non-negative matrix factorization that maximizes a Poisson likelihood subject to a penalty that accounts for sparsity in the network. By fitting the factorization model over a rolling window with a fast numerical optimization algorithm, change detection is accomplished by statistical monitoring of the matrix factors' evolution. Using synthetic and real financial interbank lending networks, we demonstrate that the proposed methodology compares favorably with alternative techniques for on-the-go network change detection.

Dynamic interbank network analysis using latent space models

Longitudinal network data are increasingly available, allowing researchers to model how networks evolve over time and to make inference on their dependence structure. In this paper, a dynamic latent space approach is used to model directed networks of monthly interbank exposures. In this model, each node has an unobserved temporal trajectory in a low-dimensional Euclidean space. Model parameters and latent banks’ positions are estimated within a Bayesian framework. We apply this methodology to analyze two different datasets: the unsecured and the secured (repo) interbank lending networks. We show that the model that incorporates a latent space performs much better than the model in which the probability of a tie depends only on observed characteristics; in particular, the latent space model is able to capture the core-periphery structure of financial networks quite well, whereas the model without a latent space is unable to do so.

Loan maturity aggregation in interbank lending networks obscures mesoscale structure and economic functions

Since the 2007–2009 financial crisis, substantial academic effort has been dedicated to improving our understanding of interbank lending networks (ILNs). Because of data limitations or by choice, the literature largely lacks multiple loan maturities. We employ a complete interbank loan contract dataset to investigate whether maturity details are informative of the network structure. Applying the layered stochastic block model of Peixoto (2015) and other tools from network science on a time series of bilateral loans with multiple maturity layers in the Russian ILN, we find that collapsing all such layers consistently obscures mesoscale structure. The optimal maturity granularity lies between completely collapsing and completely separating the maturity layers and depends on the development phase of the interbank market, with a more developed market requiring more layers for optimal description. Closer inspection of the inferred maturity bins associated with the optimal maturity granularity reveals specific economic functions, from liquidity intermediation to financing. Collapsing a network with multiple underlying maturity layers or extracting one such layer, common in economic research, is therefore not only an incomplete representation of the ILN’s mesoscale structure, but also conceals existing economic functions. This holds important insights and opportunities for theoretical and empirical studies on interbank market functioning, contagion, stability, and on the desirable level of regulatory data disclosure.

A Double-Layer Network and the Contagion Mechanism of China's Financial Systemic Risk

We studied the dynamic mechanism of systemic risk contagion in financial systems with a double-layer network and analysed the factors influencing systemic risk. We used the maximum entropy method to estimate interbank loan data and combined cross-shareholding data among financial institutions to construct a double-layer network structure of China’s financial system, consisting of an interbank lending and a cross-shareholding network. Through simulation, we analysed the nonlinear evolution of the risk contagion process. Numerical results showed that contagious losses in the double-layer network are more than the sum of the losses infecting the interbank lending network and the cross-shareholding network independently. Most of the excess losses are caused by the interbank lending channel. However, if linkages in the cross-shareholding channels are strengthened, excess losses tend to increase. Furthermore, we found that certain specific factors, i.e., the loss rate of assets, the loss given default, total assets, interbank assets and interbank liabilities, have a positive impact on the losses caused by the potential bankruptcy of an institution. We also found that the number of bankruptcies could increase significantly with an increase in the asset loss rate and the loss given default. Our findings suggest certain policy recommendations for managing systemic risk.

Interbank Runs: A Network Model of Systemic Liquidity Crunches

I study how interbank lending network structures affect financial fragility. Interbank lending is beneficial but subject to coordination failure. With interbank wholesale funding, banks' balance sheets become inflated, and give the senior retail depositors a sense of safety to allow more illiquid assets. In interbank runs, banks run on banks as they mutually reinforce each other to withdraw interbank lending. Banks' individually precautionary liquidity hoarding strategies are connected by the pairwise lending relationships. Mean-field analysis extracts the systemic behavior from the network of strategic interactions. I show such dispersed and indirectly linked interactions also lead to discontinuous and system-wide liquidity crunches, as if the interactions are centralized. Local insolvency shocks trigger the interbank run if the network is unraveled beyond a critical point. The model is applied to identify the optimal capital injection targets of government bailouts, and study the systemic effects of the proposed regulations on restraining the highly connected banks.

Harmonic distances, centralities and systemic stability in heterogeneous interbank networks

This paper investigates the effects of contagion in interbank-lending networks, with a special focus on the theoretical grounding of centrality measures. I introduce a new systemic importance measure based on the harmonic distance of Acemoglu, Ozdaglar and Tahbaz-Salehi from 2015. Then, motivated by their theoretical results for concentration centrality in a later work of the same year, I compare this measure with well-known centrality measures that have already been applied in the systemic risk literature, and that do not take into account the structure of a contagion mechanism. I derive an explicit formula of size-adjusted harmonic distances and extend it by using liquid assets for a heterogeneous banking system. The simulation results on extended Barabasi–Albert-type networks and complete networks do not confirm that these new measures would perform better than “off-the-shelf” centralities, but a linear combination of these measures forecasts losses better than any of the individual measures. The time variations of centrality measures are presented in an interbank network. I also test for the scale-free property of the Hungarian interbank-lending network and suggest that network measures be used as systemic risk indicators in systemic stress indexes. Finally, this work points out that, in some cases, the application of centrality measures might be misleading, and specific clearing algorithms provide more reliable results.

Dynamic Interbank Network Analysis Using Latent Space Models

Longitudinal network data are increasingly available, allowing researchers to model how networks evolve over time and to make inference on their dependence structure. In this paper, a dynamic latent space approach is used to model directed networks of monthly interbank exposures. In this model, each node has an unobserved temporal trajectory in a low-dimensional Euclidean space. Model parameters and latent banks' positions are estimated within a Bayesian framework. We apply this methodology to analyze two different datasets: the unsecured and the secured (repo) interbank lending networks. We show that the model that incorporates a latent space performs much better than the model in which the probability of a tie depends only on observed characteristics; the latent space model is able to capture some features of the dyadic data such as transitivity that the model without a latent space is not able to.

Interbank contagion: An agent-based model approach to endogenously formed networks

Abstract The potential impact of interconnected financial institutions on interbank financial systems is a financial stability concern for central banks and regulators. In examining how financial shocks propagate through contagion effects, we argue that endogenous individual bank choices are necessary to properly consider how losses develop as the interbank lending network evolves. We present an agent-based model to endogenously reconstruct interbank networks based on 6600 banks’ decision rules and behaviors reflected in quarterly balance sheets. We compare the results of our model to the results of a traditional stationary network framework for contagion. The model formulation reproduces dynamics similar to those of the 2007–09 financial crisis and shows how bank losses and failures arise from network contagion and lending market illiquidity. When calibrated to post-crisis data from 2011 to 2014, the model shows the U.S. banking system has reduced its likelihood of bank failures through network contagion and illiquidity, given a similar stress scenario.

Systemic Losses Due to Counterparty Risk in a Stylized Banking System

We report a study of a stylized banking cascade model investigating systemic risk caused by counter party failure using liabilities and assets to define banks' balance sheet. In our stylized system, banks can be in two states: normally operating or distressed and the state of a bank changes from normally operating to distressed whenever its liabilities are larger than the banks' assets. The banks are connected through an interbank lending network and, whenever a bank is distressed, its creditor cannot expect the loan from the distressed bank to be repaid, potentially becoming distressed themselves. We solve the problem analytically for a homogeneous system and test the robustness and generality of the results with simulations of more complex systems. We investigate the parameter space and the corresponding distribution of operating banks mapping the conditions under which the whole system is stable or unstable. This allows us to determine how financial stability of a banking system is influenced by regulatory decisions, such as leverage; we discuss the effect of central bank actions, such as quantitative easing and we determine the cost of rescuing a distressed banking system using re-capitalisation. Finally, we estimate the stability of the UK and US banking systems in the years 2007 and 2012 showing that both banking systems were more unstable in 2007 and connectedness on the interbank market partly caused the banking crisis.