Interbank Network Structure Research Articles

Systemic Risk with Multi-Channel Risk Contagion in the Interbank Market

The systematicness of banks is an important driver of financial crisis. Overlapping portfolios and assets correlation of banks’ investment are important reasons for systemic risk contagion. The existing systemic risk models are all analyzed from one aspect and cannot reflect the real situation of the banking system. In the present paper, considering the overlapping portfolios and assets correlation, a contagion network model with multi-channel risk is proposed, which is with interbank lending (direct contagion channel), overlapping portfolios (indirect contagion channel), and assets correlation (indirect contagion channel). In addition, the model takes investment risk as an impact factor and learns the operation rules of the banking system to help banks compensate for liquidity through asset depreciation. Based on the proposed model, the effects of assets correlation, assets diversity, assets investment strategy, interbank network structure, and the impact of market density on risk contagion are studied and analyzed quantitatively. The method in this paper can more truly reflect the banking system risk than the existing model. This paper provides a solution for quantitative analysis of systemic risk, which provides powerful tools for macroprudential stress testing and a reference for regulatory authorities to prevent systemic risk.

Network structure of Turkish interbank market

Global financial crisis has shown the importance of understanding the structure of interbank relations. In this study, we investigate the network relations based on interbank exposures in Türkiye. We estimate several network statistics and document how the network relations have changed over the time period of 2002–2021. We find that the network structures vary substantially by financial instruments such as repo, deposit, loan, security issuances, derivatives and other off-balance sheet items showing the significance of covering all type of exposures in network analysis. Using network statistics, we show that Turkish interbank network structure shows a core-periphery structure which is found to be more resilient during stress times in the literature. Finally, we find that larger banks are characterized as having higher network centrality measures as degree, clustering coefficient and closeness centrality showing the importance of these banks in terms of intermediation and substitutability.

The Stability of Banking System with Shadow Banking on Different Interbank Network Structures

With the rapid development of the financial market, the outbreak of systemic risk is affected by many factors, among which shadow banking is considered to be the essential reason to cause financial crisis and destroy the stability of the banking system. In view of the stability of the banking system, considering shadow banking, interbank lending, and complex relationships between banks, a dynamic complex interbank network model with shadow banking under different network structures is proposed. Based on the model, the effects of ROI, investment periods, average deposit, deposit interest rate, the density of shadow banks, and asset loss are studied quantitatively, and the sensitivity and difference of the banking system with shadow banking under different interbank networks are compared and analyzed. The findings indicate that the spread of systemic risks between banks is closely related to the interbank network structures. With the relatively concentrated interbank network structure, it is easier to increase the probability and degree of risk contagion. Under the random, small-world, and scale-free networks, the random network has the strongest ability to resist and absorb risks, while the small-world network is the weakest. However, once the banking network suffers a big shock, excessive risk will directly break through the protection of the banking network, detonate the systematic risk, and destroy the stability of the banking system with shadow banking. This study contributes to a future empirical research agenda on the topic. Moreover, it gives a reference for policymakers and regulatory authorities to prevent systemic risk introduced by shadow banking.

Optimal Bailouts and the Doom Loop with a Financial Network

Banks usually hold large amounts of domestic public debt which makes them vulnerable to their own sovereign’s default risk. At the same time, governments often resort to costly public bailouts when their domestic banking sector is in trouble. We investigate how the interbank network structure and the distribution of sovereign debt holdings jointly affect the optimal bailout policy in the presence of this doom loop. Rescuing banks with high domestic sovereign exposure is optimal if these banks are sufficiently central in the network, even though that requires larger bailout expenditures than rescuing low-exposure banks. Our findings imply that highly central banks can use exposure to their own government as a strategic tool to increase the likelihood of being bailed out. Our model thus illustrates how the doom loop exacerbates the too interconnected to fail problem in banking. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Do banking groups shape the network structure? Evidence from Turkish interbank market

Using a unique exposure data on Turkish banking system (disaggregated according to bank ownership structure and instrument level), we study the effects of ownership structure on the interbank network structure. During the sample period of 2003–2017, we observe that foreign and state deposit banks are the dominant institutions shaping the structure of the network under different contracts. Foreign banks, in particular, have a higher coreness vector in derivative exposures through their comparative advantage in offsetting derivative transactions. Moreover, our findings suggest that when foreign investors acquire domestic banks, the network structures of the acquired bank change considerably. We also present evidence that local and Basel III regulations play a significant role in the formation of the network structure through liquidity channel. We found highly liquid large banks and well-capitalized non-large banks have become more centralized in the interbank security market. The central bank's efforts to restore liquidity in the money markets during and after the global financial crisis seem to have increased the central role of foreign deposit banks in the overnight lending market against state and large private deposit banks.

The interbank payment network and financial system stability

Purpose The financial turbulence resulting from the global financial crisis sparked the interest in improving understanding of financial risks. The transmission of financial institution failures can be determined from the prevailing network structures between banks. The purpose of this study is to identify relationship between payment system network characteristics and financial system condition. Design/methodology/approach The characteristics of the interbank network structure in the payment system are identified using a graph theory and the relationship between the network characteristics of interbank transactions in the payment system and financial system stability is examined using a vector auto regression model. Findings This study shows that the connectedness of large-value payment transaction is more segmented compared to that of retail value payments. A significant relationship is observed between the characteristics of the network and the large-value payment transactions. Research limitations/implications This study found the connectedness of large-value transactions is more segmented when compared to retail-value transactions. It also shows a causal effect of the network characteristic on the financial system stability. Originality/value Unlike existing studies, this study considers both the connectedness in large-value transactions and retail-value transactions.

Macroprudential regulation for a dynamic Chinese banking system with a scale-free network

The frequent global financial crises in recent years show that it is necessary to implement macroprudential regulation for the banking system. At present, quantitative research on the macroprudential regulation for the dynamic Chinese banking network system is lacking, while the related studies in other countries have not considered the interbank network structure. Therefore, in the present paper, we construct a dynamic banking network model with a scale-free network and a dynamic macroprudential regulation model under four risk allocation mechanisms (CVaR, Incremental VaR, Shapley value EL, and ΔCoVaR) for the dynamic Chinese banking network system. Then, we conduct empirical research to study the effect of the macroprudential regulation model on the Chinese banking network system. Our results show that the Chinese banking network system was the most unstable in 2010 and that the average default probability decreased every year after the macroprudential regulation, indicating the effectiveness of the macroprudential regulation model. From the perspective of the scale-free network structure, we find that the intrinsic mechanism of macroprudential regulation is to rewire the interbank linkages from small banks to large banks with more interbank lending to prevent contagious risk, thereby improving the stability of the entire banking system. Moreover, the regulation effects of ΔCoVaR and CVaR mechanisms are found to be better than those of the other mechanisms. The regulation effect of ΔCoVaR is the most significant.

Collateralized-Uncollateralized Funding Decision in Money Markets

Little is known about the factors that affect banks' relative preferences for collateralized and uncollateralized funding. We examine the determinants of the collateralized-uncollateralized funding decision using a unique dataset. We decompose the relative collateralized-uncollateralized funding activity into migrations and co-movements, including the directions of funding flows. Migrations and co-movements depend on various determinants, such as systemic risk, collateral availability and the inter-bank network structure. Interestingly, migrations and co-movements behave differently in periods of high financial stress, because safe assets (used as collateral to reduce counterparty risk) are then no longer perceived as safe as during periods of low-moderate financial stress.

Financial Contagion in the Laboratory: Does Network Structure Matter?

AbstractWe explore the role of interbank network structure and premature liquidation costs for the likelihood of financial contagions in a laboratory experiment. We consider complete versus incomplete networks of banks linked together by interbank deposits, and we further vary premature liquidation costs. Subjects play the role of depositors deciding whether or not to withdraw funds from their interconnected bank. We find that when liquidation costs are high, a complete network structure is significantly less vulnerable to financial contagions than an incomplete network structure. However, when liquidation costs are low, network structure is less important for the frequency of financial contagions.

A Minimal Agent-Based Model Reproduces the Overall Topology of Interbank Networks

The description of the empirical structure of interbank networks constitutes an important field of study since network theory can be used as a powerful tool to assess the resilience of financial systems and their robustness against failures. On the other hand, the development of reliable models of interbank market structure is relevant as they can be used to analyze systemic risk in the absence of transaction data or to test statistical hypotheses regarding network properties. Based on a detailed data-driven analysis of bank positions (assets and liabilities) taken from the Bankscope database, we here develop a minimal, stochastic, agent-based network model that accounts for the basic topology of interbank networks reported in the literature. The main assumption of our model is that loans between banks attempt to compensate assets and liabilities at each time step, and the model renders networks comparable with those observed in empirical studies. In particular, our model is able to qualitatively reproduce degree distributions, the distribution of the number of transactions, the distribution of exposures, the correlations with nearest-neighbor out-degree, and the clustering coefficient. As our simple model captures the overall structure of empirical networks, it can thus be used as a null model for testing hypotheses relative to other specific properties of interbank networks.

Network models of financial systemic risk: a review

The global financial system can be represented as a large complex network in which banks, hedge funds and other financial institutions are interconnected to each other through visible and invisible financial linkages. Recently, a lot of attention has been paid to the understanding of the mechanisms that can lead to a breakdown of this network. This can happen when the existing financial links turn from being a means of risk diversification to channels for the propagation of risk across financial institutions. In this review article, we summarize recent developments in the modeling of financial systemic risk. We focus, in particular, on network approaches, such as models of default cascades due to bilateral exposures or to overlapping portfolios, and we also report on recent findings on the empirical structure of interbank networks. The current review provides a landscape of the newly arising interdisciplinary field lying at the intersection of several disciplines, such as network science, physics, engineering, economics, and ecology.

Network Models of Financial Systemic Risk: A Review

The global financial system can be represented as a large complex network in which banks, hedge funds and other financial institutions are interconnected to each other through visible and invisible financial linkages. Recently, a lot of attention has been paid to the understanding of the mechanisms that can lead to a breakdown of this network. This can happen when the existing financial links turn from being a means of risk diversification to channels for the propagation of risk across financial institutions. In this review article, we summarize recent developments in the modeling of financial systemic risk. We focus in particular on network approaches, such as models of default cascades due to bilateral exposures or to overlapping portfolios, and we also report on recent findings on the empirical structure of interbank networks. The current review provides a landscape of the newly arising interdisciplinary field lying at the intersection of several disciplines, such as network science, physics, engineering, economics, and ecology.

A Minimal Agent-Based Model Reproduces the Overall Topology of Interbank Networks

The description of the empirical structure of interbank networks constitutes an important field of study since network theory can be used as a powerful tool to assess the resilience of financial systems and their robustness against failures. On the other hand, the development of reliable models of interbank market structure is relevant as they can be used to analyze systemic risk in the absence of transaction data or to test statistical hypotheses regarding network properties. Based on a detailed data-driven analysis of bank positions (assets and liabilities) taken from the Bankscope database, we here develop a minimal, stochastic, agent-based network model that accounts for the basic topology of interbank networks reported in the literature. The main assumption of our model is that loans between banks attempt to compensate assets and liabilities at each time step, and the quarterly aggregation of daily networks yields structures comparable with those observed in empirical studies. In particular, our model is able to qualitatively reproduce degree distributions, the distributions of transactions by value and transactions by volume, the distribution of exposures (link weights), the correlations with nearest-neighbor averages, and the local structure (clustering coefficient). As our simple model captures the overall structure of empirical networks, it can thus be used as a null model for testing hypotheses relative to other specific properties of interbank networks.

A dynamic network model for interbank market

Abstract In this paper, a dynamic network model based on agent behavior is introduced to explain the formation mechanism of interbank market network. We investigate the impact of credit lending preference on interbank market network topology, the evolution of interbank market network and stability of interbank market. Experimental results demonstrate that interbank market network is a small-world network and cumulative degree follows the power-law distribution. We find that the interbank network structure keeps dynamic stability in the network evolution process. With the increase of bank credit lending preference, network clustering coefficient increases and average shortest path length decreases monotonously, which improves the stability of the network structure. External shocks are main threats for the interbank market and the reduction of bank external investment yield rate and deposits fluctuations contribute to improve the resilience of the banking system.

Filtering for Risk Assessment of Interbank Network

We develop a framework for risk assessment in an interbank network, where banks interact with each other via short-term debt contracts. Focusing on the demand side of liquidity and omitting credit rationing, the framework identifies the interbank network structure and its degree of interconnectedness. Identification is facilitated by a statistical learning procedure that reverse engineers signals (transactions) observed in the interbank market and conducts inference about banks’ individual and network-level characteristics. Performing a series of simulation studies after banking network identification, we measure the interconnectedness of the network with respect to its resilience to absorb systemic risk. The findings from the simulations imply that the benefits of integration outweigh the cost of interconnectedness as long as the interconnectedness technology is hedge serving (risk diversifying). If it is not the case, the simulation risk metrics we compute increase exponentially with the level of network interconnectedness. This, as a result, undermines the value of integration, even when the network is identified in its simplest forms.

Too interconnected to fail: a survey of the interbank networks literature

The banking system is highly interconnected and these connections can be conveniently represented as an interbank network. This survey presents a systematic overview of the recent advances in the theoretical literature on interbank networks. We assess our current understanding of the structure of interbank networks, of how network characteristics affect contagion in the banking system and of how banks form connections when faced with the possibility of contagion and systemic risk. In particular, we highlight how the theoretical literature on interbank networks offers a coherent way of studying interconnections, contagion processes and systemic risk, while emphasizing at the same time the challenges that must be addressed before general results on the link between the structure of the interbank network and financial stability can be established. The survey concludes with a discussion of the policy relevance of interbank network models with a special focus on macroprudential policies and monetary policy.

Testing Information Diffusion in the Decentralized Unsecured Market for Euro Funds

Average rates in the decentralized unsecured market for euro funds, like the EONIA for the overnight maturity, are fundamental indicators of the smooth transmission of the signal rate by the central bank. Public information plays an important role in this context, as key interest rates are set by the central bank and average market rates are published daily, constituting common knowledge. Nevertheless, according to the theoretical literature on over-the-counter markets, private information may have an important role in a decentralized market. The diffusion of private information can generate prices that depend on the decentralized market structure. This is the first paper to use an ad hoc (network) version of the spatial autoregressive model to assess the presence of this mechanism. I propose a simple methodology to test whether the joint distribution of rates depends on the interbank network structure and to estimate information diffusion strength. The method is applied to a unique dataset collecting unsecured interbank loans and characteristics of banks operating in European central bank money. A wide time span including sovereign debt crises in the euro area is considered. I find that information diffusion played a greater role during periods dominated by strong uncertainty.

A Counterparty Risk Study of UK Banking Systems

We study the effect of counterparty risk on the stability of a banking system using stylized banking cascade models calibrated with UK exposure and balance sheet data from regulatory reports. We observe the development of a fragile phase, at which small perturbations to banks' capital reserves can trigger a sudden system failure. We compute the critical balance sheet values for a stylized homogeneous system and show that a more realistic heterogeneous system, with different bank types and complex interbank network calibrated on UK data, also has systemic failures around similar sized shocks to banks' capital as the stylized homogeneous system. By aggregating the fraction of surviving banks to specific bank types, we show that balance sheet size and banks' position in the interbank network are not the decisive components when considering system failure due to counterparty insolvency. This implies that interbank network structure and heterogeneity do not play a critical role in the onset of the fragile phase. Instead, the onset of the system failure depends on the ratios of interbank assets to total assets and loss absorbing capital to total assets. Finally, we discuss the effects of increased exposure of specific banks and we compute minimum capital requirements to ensure a stable banking system.

Too Interconnected to Fail: A Survey of the Interbank Networks Literature

The banking system is highly interconnected and these connections can be conveniently represented as an interbank network. This survey presents a systematic overview of the recent advances in the theoretical literature on interbank networks and assesses our current understanding of the structure of interbank networks, of how network characteristics affect contagion in the banking system and of how banks form connections when faced with the possibility of contagion and systemic risk. In particular, I highlight how the theoretical literature on interbank networks offers a coherent way of studying interconnections, contagion processes and systemic risk, while emphasizing at the same time the challenges that must be addressed before general results on the link between the structure of the interbank network and financial stability can be established. The survey concludes with a discussion of the policy relevance of interbank network models with a special focus on macro-prudential policies and monetary policy.

The multiplex structure of interbank networks

The interbank market has a natural multiplex network representation. We employ a unique database of supervisory reports on Italian banks to the Banca d’Italia that includes all bilateral exposures broken down by maturity and by the secured and unsecured nature of the contract. We find that layers have different topological properties and persistence over time. The presence of a link in a layer is not a good predictor of the presence of the same link in other layers. Maximum entropy models reveal different unexpected substructures, such as network motifs, in different layers. Using the total interbank network or focusing on a specific layer as representative of the other layers provides a poor representation of interlinkages in the interbank market and could lead to biased estimation of systemic risk.