Interbank Market Network Research Articles

Research on systemic risk in a triple network

At present, most research on systemic risk focuses on a single interbank market network or bank-asset bipartite network, but the research on how multiple networks interact and affect the systemic risk is still rare. Therefore, this paper constructs a triple network to study the systemic risk, which includes the interbank market network, the bank-asset bipartite network, and the asset-asset relationship network. Different from previous studies using the number of bank failures to measure the systemic risk, this paper uses the DebtRank algorithm to construct the feedback effect between networks and further studies the influence of network average degree and network crowding on the systemic risk under the feedback effect of the triple network. The results show that: (i) The feedback effect between the interbank market network and the bank-asset bipartite network is the accelerator of the systemic risk. (ii) Under any three network properties, the systemic risk increases with the increase of network crowding. (iii) Under different network average degrees, the nature of triple networks is different and the magnitude of the systemic risk is different. In the case of the positive asset-asset network, the larger interbank market network, and the smaller bank-asset bipartite network, the smaller the systemic risk; however, when the asset-asset network is a non-positively relationship (negative and random relationship), if the interbank market network is large, and the bank-asset bipartite network is small, then the systemic risk is large. (iv) When the asset-asset network has a positive relationship, regardless of the size of the interbank market network and the bank-asset bipartite network, the systemic risk increases with the increase of the network average degree; when the asset-asset network has a non-positive relationship (negative correlation and random relationship), the systemic risk first decreases with the increase of network average degree, then decreases to the critical value, and then increases with the increase of network average degree. The model and analysis method of this paper provides a framework for the quantitative research of systemic risk and provides a decision-making basis for regulators and policymakers.

Research on the Stock Correlation Network Entropy and Its Influencing Factors in the Chinese Interbank Market

The stability of the interbank market is an essential guarantee for the sustainable development of society. Network entropy theory provides a critical research paradigm for the study of the stability of the interbank market. Considering that few scholars have conducted in-depth analysis on the influencing factors of the network entropy’s evolution process, this paper constructs the stock correlation network entropy model in the Chinese interbank market based on the correlation of stock price fluctuation. It focuses on analyzing the influencing factors in the dynamic evolution process of the stock correlation network entropy. In the light of empirical research, we can obtain the following results. First, the interbank market network’s aggregation coefficient, the interbank market network’s centrality, and the bank stock’s return rate play a positive role in the dynamic evolution of stock correlation network entropy in the Chinese interbank market. Second, the bank stock return’s volatility is negatively correlated with its network entropy. To maintain the stability of the financial market, the supervision department can monitor the stability of the interbank market by constructing the stock correlation network entropy.

Finding the Network Structure of Rwandan Interbank Market

The objective of this paper was to analyze the topology of interbank network in Rwanda for policy formulation. Our main result is that interbank market network in Rwanda is described by a core- periphery model with some level of completeness of interbank market in Rwanda. As policy implication, any risk from a bank is more easily shared within the interbank market network provided that there is nearly a complete network. This is an indication that the risk of instability of the financial system in Rwanda originating from interbank market is limited.

Network ANOVA random effects models for node attributes

This paper develops a subgraph network random effects error components structure for network data to perform analysis of variance. In particular, it proposes a model for evaluating the network interdependence of nodes attributes allowing for edge and triangle specific components. The latter serve as a basal model for modeling more general network effects. Consistent estimators of the variance components and Lagrange Multiplier specification tests for evaluating the appropriate model of random components in networks structures is proposed. Monte Carlo simulations show that the tests have good performance in finite samples. The proposed tests is applied to the unsecured (Call) interbank market network in Argentina.

The Network Structure of the Malawi Interbank Market: Implications for Liquidity Distribution and Contagion around the Banking System

The study aimed to understand the network structure of the Malawi interbank market, a relatively small but active market. To do this, we constructed a network of banks using aggregated interbank loan amounts. We then analyzed the topological characteristics of the interbank market network structure and discussed the implications of such characteristics in terms of liquidity distribution and contagion in the Malawi banking system. We establish that the Malawi’s interbank network is fairly dense with a significantly high clustering and a small average path length. This implies that liquidity is able to flow in a fairly efficient manner within the network. Due to the relatively high connectivity of the network, entry or exit of a bank, on average, is likely to have little impact on the ability of other banks to lend and borrow from each other. The high connectivity further implies that banks are able to monitor each other’s behaviour. This may result into liquidity hoarding and may force some banks to get liquidity at a higher cost than the one prevailing on the market. The relatively high clustering and a small average path length further implies that the interbank participants are more vulnerable to contagion than in random networks. We further argue that because of the strong connectivity, the network may not be resilient to an operational shock affecting one or more of the banks. In this case, the impact of an operational shock may be felt not just on the connectivity of the network but rather on the availability of liquidity within the banking system.

Network model of credit risk contagion in the interbank market by considering bank runs and the fire sale of external assets

We construct a network model of credit risk contagion in the interbank lending market based on time series. Such network model is based on imperfect information considering that the initial credit default of the debt bank will cause the creditor bank to run on its debt bank and the debt bank’s fire sale of external assets held to repay the debt or control the leverage ratio that reduce asset prices and create spillovers. By the theoretical deduction and simulation method, we study how the contagion effects of credit risk accumulate and spread in the interbank market network. In addition, we study the evolution characteristics of credit risk contagion caused by the initial default of debt banks in the interbank market. The following are the results of the study. First, when banks sell external assets at a fixed price, the fire sale of external assets can effectively reduce the probability of credit default. However, when the price of external assets decrease with the increase of fire sales volume, banks’ fire sale of external assets will lead to more serious contagion spillover consequences than that of none of the banks’ selling strategies. Second, the panic of bank decision makers in the banking system has a nonlinear evolution characteristic of increasing and decreasing credit risk contagion. Third, the higher the proportion of banks’ liquid assets, the stronger their ability to resist credit risks. Last, the higher the proportion of banks’ interbank assets, the stronger the contagion effect of credit risk on the entire banking system.

MULTI-CHANNEL CONTAGION IN DYNAMIC INTERBANK MARKET NETWORK

In this paper, a dynamic interbank market network model based on bank agent behaviors is developed to analyze financial contagion with counter-party and liquidity channels. Afterwards, we analyze the impact of dynamics on the stability of interbank market and find that dynamics of interbank market could enhance the resilience of the network, which suggests contagion might be overestimated in current studies. Moreover, we investigate the mechanism of contagion when counter-party and liquidity channels are both active in the dynamic interbank market network. Specifically, we analyze the effects of bank capitalization, interbank exposures, liquid assets, and bank credit lending preference on the stability of the banking system, respectively. First, we find that liquidity in interbank market and fluctuations of deposits could amplify the negative impact of each other on the resilience of interbank market network. Second, banks with higher capitalization level tend to be more resilient against financial contagion. Third, interbank exposures may have multiple effects on the resilience of interbank market network. Fourth, the resilience of interbank market network is a nonmonotonic function of percentage of liquid assets. Finally, we discover a complex relationship between bank credit lending preference and the resilience of interbank market.

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.

The Stability of Interbank Market Network: A Perspective on Contagion and Risk Sharing

As an important part of the financial system, interbank market provides banks with liquidity and credit lending and also is the main channel for risk contagion. In this paper, we test the existence of systematic risk contagion within the Chinese interbank market. By building the networks of the Chinese interbank market for each year and using the measure of mutual information, we quantitatively detect the changes of interbank market networks and observe that the correlations between banks become increasingly tighter in recent years. With the bilateral risk exposure among Chinese listed commercial banks, we find that the possibility of systemic risk contagion in Chinese interbank market is fairly small. But of great concern on each individual bank, the matter is different. Our simulation shows that the failures of three special banks (i.e., Agricultural Bank of China and Bank of China and Industrial and Commercial Bank of China) most likely lead to systemic risk contagion. Furthermore, we test the antirisk ability of the Chinese interbank market from the perspective of risk sharing and discover that the interbank market is stable when the loss scale is lower than forty percent of banks’ total core capital.

A Dynamic Stochastic Network Model of the Unsecured Interbank Lending Market

This paper introduces a structural micro-founded dynamic stochastic network model for the unsecured interbank lending market. Banks are profit optimizing agents subject to random liquidity shocks and can engage in costly counterparty search to find suitable trading partners and peer monitoring to reduce counterparty risk uncertainty. The structural parameters are estimated by indirect inference using appropriate network statistics of the Dutch interbank market. The estimated model is shown to explain accurately important dynamic features of the interbank market network. In particular, monitoring of counterparty risk and directed search are shown to be key factors in the formation of interbank trading relationships that are associated with improved credit conditions. Finally, the model is used to filter the optimal search and monitoring expenditures in the network and to analyze optimal network responses to changes in the policy of the central bank.

Fitness model for tiered structure in the interbank market

Tiered structure is observed in a range of countries' banking systems. In that case, relatively few first-tier banks are not only interconnected, but are connected with second-tier banks, whereas second-tier banks are almost exclusively connected with first-tier banks. This study uses the theory of complex networks to quantitatively characterize the formation of tiered structure in banking systems. The interbank market network model constructed in this article reproduces tiered structure and various statistical properties, namely, a small-world property and a disassortative mixing property as well as a reciprocal property. This network modeling of the interbank market could be an efficient way to understand the bank behavior in the interbank market. © 2012 Wiley Periodicals, Inc. Complexity, 2012

Resilience of Interbank Market Networks to Shocks

This paper first constructs a tiered network model of the interbank market. Then, from the perspective of contagion risk, it studies numerically the resilience of four types of interbank market network models to shocks, namely, tiered networks, random networks, small‐world networks, and scale‐free networks. This paper studies the interbank market with homogeneous and heterogeneous banks and analyzes random shocks and selective shocks. The study reveals that tiered interbank market networks and random interbank market networks are basically more vulnerable against selective shocks, while small‐world interbank market networks and scale‐free interbank market networks are generally more vulnerable against random shocks. Besides, the results indicate that, in the four types of interbank market networks, scale‐free networks have the highest stability against shocks, while small‐world networks are the most vulnerable. When banks are homogeneous, faced with selective shocks, the stability of the tiered interbank market networks is slightly lower than that of random interbank market networks, whereas, in other cases, the stability of the tiered interbank market networks is basically between that of random interbank market networks and that of scale‐free interbank market networks.