The effect of the interbank network structure on contagion and common shocks

This paper proposes a dynamic multi-agent model of a banking system with central bank. Banks optimize a portfolio of risky investments and riskless excess reserves according to their risk, return, and liquidity preferences. They are linked via interbank loans and face stochastic deposit supply. Comparing different interbank network structures, it is shown that money-centre networks are more stable than random networks. Evidence is provided that the central bank stabilizes interbank markets in the short run only. Systemic risk via contagion is compared with common shocks and it is shown that both forms of systemic risk require different optimal policy responses.     

The formation of financial networks

Modern banking systems are highly interconnected. Despite various benefits, linkages between banks carry the risk of contagion. In this article, I investigate whether banks can commit ex ante to mutually insure each other, when there is contagion risk in the financial system. I model banks' decisions to share this risk through bilateral agreements. A financial network that allows losses to be shared among various counterparties arises endogenously. I characterize the probability of systemic risk, defined as the event that contagion occurs conditional on one bank failing, in equilibrium interbank networks. I show that there exist equilibria in which contagion does not occur.     

Credit Risk in Interbank Networks

ABSTRACT

One of the most striking characteristics of modern financial systems is their complex interdependence, comprising a network of bilateral exposures in the interbank market, in which institutions with surplus liquidity can lend to those with a liquidity shortage. Empirical studies reveal that some interbank networks have features of scale-free networks. We explore the characteristics of financial contagion in networks whose distribution of links approaches a power law, using a model that defines banks’ balance sheets from information on network connectivity. By varying the parameters for the creation of the network, several interbank networks are built, in which the concentration of debt and credit comes from the distribution of links. The results suggest that networks that are more connected and have a high concentration of credit are more resilient to contagion than other types of networks analyzed.     

Systemic liquidity shortages and interbank network structures

This paper aims to shed light on the systemic nature of liquidity risk and to propose a method for calculating systemic liquidity shortages. Our method incorporates not only direct liquidity shortages but also indirect liquidity shortages due to the knock-on effects through interbank linkages. We perform a simulation with a simple banking system model and find that a deficit bank can mitigate a liquidity shortage by holding more claims on a surplus bank. Meanwhile, a greater imbalance in liquidity positions across banks tends to aggravate the liquidity shortage of a deficit bank. According to comparative analysis between different types of network structures, a core-periphery network with a deficit money center bank gives rise to the highest level of systemic liquidity shortage, and a banking system becomes more vulnerable to liquidity shocks as its interbank network becomes more ill-matched.     

基于共同冲击和异质风险叠加传导的风险传染研究——来自中国上市银行网络的传染模拟

本文将银行系统遭遇外部共同冲击作为研究起点,建立了一个共同冲击和异质风险交互传导与放大的简化模型,冲击的传导包括“原始冲击”、“增量冲击”和“违约冲击”三个风险传染阶段。基于2018年我国15家上市银行的股票收益率和年报数据、2006年至2018年的银行评级数据,本文构建了贝叶斯分层图模型和银行间拆借矩阵,并利用蒙特卡洛模拟测度不同触发银行所引发的系统性风险损失、单个银行的系统性风险杠杆能力(文中定义为“传染乘数”指标)以及政府监管介入的效果。模拟结果显示:共同冲击损失远大于异质风险损失;规模和网络关联性是决定传染乘数的重要因素,且当规模因素不突出时,网络关联性对传染乘数的决定作用相对更强,极容易出现小规模、高关联性银行具有较高的传染乘数;当银行风险资产损失率在10%至25%之间时,造成系统性风险损失的杠杆能力普遍增强;政府监管介入能较好地降低系统性风险。本研究的相关结论为系统性风险的监管设计提供经验证据和参考。     

A network model of systemic risk: stress testing the banking system1

Although there are many definitions of systemic risk, most agree that it manifests itself by an initial shock that results in the failure of one or more banks and then spreads out to the entire system by a contagion mechanism which can result in the failure of more banks in the system. Assuming that bank failures in the initial shock are randomly dependent on the failure probabilities of the individual banks and that the ensuing contagion process is deterministic, depending on interbank exposures, in this paper we propose a network model to analyse systemic risk in the banking system that, in contrast to other proposed models, seeks to obtain the probability distribution of losses for the financial system resulting from the shock/contagion process. Thus, calculating the probabilities of joint failures by simulation and assuming that the matrix of bilateral interbank exposures is known, we represent systemic risk in the financial system by means of a graph and use discrete modelling techniques to characterize the dynamics of contagion and corresponding losses within the network. The probability distribution of losses, risk profile for the Mexican banking system, is obtained through an efficient, complete enumeration procedure of all possible bank default events in the system. This, in turn, allows the use of the wide variety of well-established risk measures to describe the fragility of the financial system. Additionally, the model allows us to perform stress tests along both the bank default probabilities and the interbank exposures and is used to assess the risk of the Mexican banking system. Copyright © 2009 John Wiley & Sons, Ltd.     

Concentrated commonalities and systemic risk in China's banking system: A contagion network approach

China's banking system has seen increasing convergence in exposures to different asset types. These concentrated commonalities have far reaching implications on systemic financial risk. Based on the commonality structure of banks' balance sheets, we construct a bipartite financial network and design novel indicators to analyze the systemic risk of China's banking system and its relation to the real economy. Results show that (1) considering the interconnectivity, indirect loss arising from common exposure is much higher than direct loss; (2) concentrated common exposure to mortgage is demonstrated to be the most significant source of systemic vulnerability in China; (3) the derived contagion network shows the property of “small world”, which plays an important role in generating systemic risk, amplified non-linearly by multi-rounds of contagion; (4) a bank's systemic importance/vulnerability depends on complex interaction between asset volume, leverage rate, and its commonality of asset composition to other banks. The model proposed in this study provides a new structural perspective to investigate the systemic risk, and a macroprudential instrument to complement existing stress testing that contributes to more efficient and precise regulations. Moreover, we contribute a ranking framework to assess each bank's systemic importance as well as vulnerability corresponding to specific real sectors.     

Social networks in the global banking sector

We show that banks with shared social connections partner more often in the global syndicated loan market and that central banks in the network play dominant roles in various interbank transactions, indicating that social connections facilitate business connections. However, more centralized banks in the network also contribute significantly to the global systemic risk. Moreover, we find the soft information generated by social networks is particularly valuable when potential partners operate under different accounting and regulatory standards. Finally, we show that the recent banking crisis significantly limited the positive soft information effects of social networks in the global banking system.     

The multi-layer network nature of systemic risk and its implications for the costs of financial crises

The inability to see and quantify systemic financial risk comes at an immense social cost. Systemic risk in the financial system arises to a large extent as a consequence of the interconnectedness of its institutions, which are linked through networks of different types of financial contracts, such as credit, derivatives, foreign exchange, and securities. The interplay of the various exposure networks can be represented as layers in a financial multi-layer network. In this work we quantify the daily contributions to systemic risk from four layers of the Mexican banking system from 2007 to 2013. We show that focusing on a single layer underestimates the total systemic risk by up to 90%. By assigning systemic risk levels to individual banks we study the systemic risk profile of the Mexican banking system on all market layers. This profile can be used to quantify systemic risk on a national level in terms of nation-wide expected systemic losses. We show that market-based systemic risk indicators systematically underestimate expected systemic losses. We find that expected systemic losses are up to a factor of four higher now than before the financial crisis of 2007–2008. We find that systemic risk contributions of individual transactions can be up to a factor of one thousand higher than the corresponding credit risk, which creates huge risks for the public. We find an intriguing non-linear effect whereby the sum of systemic risk of all layers underestimates the total risk. The method presented here is the first objective data-driven quantification of systemic risk on national scales that reveal its true levels.     

Multilayer financial networks and systemic importance: Evidence from China

In this paper, we develop a multilayer network structure and reveal the relationship between network structure and systemic risk. Unlike many previous studies, our model considers both liability and cross-holding of shares between financial institutions simultaneously. We propose a new systemic risk measurement by exploring the dynamic mechanism of financial contagion in the multilayer network. We display the network structure of Chinese financial institutions, including connectivity and diversity, and identify the systemic importance of them. We demonstrate that the multilayer network plays a non-linear role in financial risk spreading. Using the panel regression model and several experiment evidences, we show that the systemic risk can be explained more effectively by the linkage diversity more than the connectivity at both the institutional level and the system level. Our results highlight the importance of considering contagion mechanisms that go beyond a simple single-layer network structure.