Interbank Contagion Research Articles

On the optimal control of interbank contagion in the euro area banking system

In this paper we present a methodology of model-based calibration of additional capital needed in an interconnected financial system to minimize potential contagion losses. Building on ideas from combinatorial optimization tailored to controlling contagion in case of complete information about an interbank network, we augment the model with three plausible types of fire sale mechanisms. We then demonstrate the power of the methodology on the euro area banking system based on a network of 373 banks. On the basis of an exogenous shock leading to defaults of some banks in the network, we find that the contagion losses and the policy authority’s ability to control them depend on the assumed fire sale mechanism and the fiscal budget constraint that may or may not restrain the policy authorities from infusing money to halt the contagion. The modelling framework could be used both as a crisis management tool to help inform decisions on capital/liquidity infusions in the context of resolutions and precautionary recapitalizations or as a crisis prevention tool to help calibrate capital buffer requirements to address systemic risks due to interconnectedness.

Optimizing systemic risk through credit network reconstruction

Capital buffers can improve the stability of the banking system, but they come at a cost. This study explores the efficient macro-prudential regulation of systemic risk from the perspective of capital network reconstruction. A model of option pricing is proposed to determine the market value of sector credits. Consequently, systemic risk is measured by both credit and interbank contagion risks. Credit network reconstruction is then optimized to minimize systemic risks. Our analysis was based on data from China's banking systems between 2008 and 2020. In different stress tests, the credit network reconstruction generally optimizes systemic risk to the lowest level. At the same time, it may save about 20% to 140% of the cost of capital. The optimization mechanism analysis shows that large banks with stability advantages share more shocks than small banks by reconstructing their sector credit network. As a result, the contagion process of systemic risk is prevented, and bankruptcy cascades are eliminated. These results imply that credit network reconstruction holds great potential for preventing systemic risks.

FinTech and the COVID-19 pandemic: Evidence from electronic payment systems

This paper investigates the effects of the coronavirus disease 2019 (COVID-19) pandemic on financial institutions and on consumers' adoption of Financial Technology (FinTech) for payments. This paper documents the following findings in Kenya. (1) The COVID-19 pandemic accelerated the adoption and increased the payment concentration of FinTech. We document an approximately 54% increase in mobile banking transactions, a 19.56% increase in mobile banking agents, and a 14.56% increase in the number of mobile banking accounts. (2) The use of all types of electronic payment cards declined significantly during the pandemic. (3) The pandemic magnified interbank contagion and liquidity risks and reduced both domestic and international electronic fund transfers via both the Real-Gross Settlement System and the Automated Clearing House. Overall, our results indicate that FinTech not only partially alleviated the negative impact of the COVID-19 pandemic during Q1 of 2021 but also accelerated consumers' adoption of FinTech and digital onboarding, especially in Q3 and Q4 of 2022.

Multi-Dimensional Factor Correlation, Multiple Interbank Network Contagion, and Conditional VaR of Banks

The systemic risk of banks is a multi-dimensional factor correlation and multi-interbank contagion, forming a multi-dimensional multi-correlation (MDMC) contagion risk. After superposition, the multiplier effect magnifies the destructive power of a single impact. In this study, the weighted average method is used to integrate the four interbank contagion paths of jump-diffusion, interbank lending, stock price information, and common assets, establish the multi-level interbank contagion matrix, apply the quantile estimation of the spatial Dubin panel model, estimate the MDMC infectious bank conditional risk value, and decompose and identify the systemically important risk factors, systemically important banks, and systemically vulnerable banks. The following conclusions were drawn. First, the superposition contagion effect of MDMC networks is significant. Second, the systemic importance of default risk, interest rate risk, liquidity risk, and the GDP growth rate of the banking industry is high, followed by changes in stock market returns and investor sentiment. Third, the four major state-owned banks have an MDMC network contagion effect, which has the characteristics of systemic importance and vulnerability.

Interbank contagion risk in China under an ABM approach for network formation

AbstractThis paper uses an agent‐based model to construct an interbank network for the Chinese interbank market using a sample of 299 commercial banks from 2014 to 2019. We analyze the importance and vulnerability of banks using the DebtRank algorithm. Our results show that the Chinese interbank market bears a certain level of systemic risk, especially among lower‐tiered banks. The results also show a bank is more vulnerable if it has a higher interbank lending ratio and greater financial connectivity. Meanwhile, a bank is more influential if it has a larger net worth and greater financial connectivity.

Contagion accounting in stress-testing

We provide a simple and tractable accounting-based stress-testing framework to assess loss dynamics in the banking sector, in a context of leverage targeting. Contagion can occur through direct interbank exposures, and indirect exposures due to overlapping portfolios with the associated price dynamics via fire sales. We apply the framework to three granular proprietary ECB datasets, including an interbank network of 26 large euro area banks as well as their overlapping portfolios of loans, derivatives and securities. A 5 percent shock to the price of assets held in the trading book leads to an initial loss of 30 percent of system equity and an additional loss of 1.3 percent due to fire sales spillovers. Direct interbank contagion is negligible in our analysis. Our findings underscore the importance of accurately estimating the price effects of fire sales.

Catastrophic and systemic risk in the non-life insurance sector: A micro-structural contagion approach

Borrowing from the interbank contagion literature, we propose a model to study the stability of non-life insurance sector in presence of catastrophic events. These events are increasingly common, and cause a large amount of damage in short periods. To account for this risk we introduce random and correlated reinsurance claims. We show in a simulation study that the sector is particularly sensitive to random correlated insurance claims, and the threat of systemic risk emerges. The risk persists even with highly diversified network structures. The work is relevant for regulators to define macro-prudential policies, and for practitioners to measure credit risk.

Interconnected banks and systemically important exposures

We study the impact of the interplay between the structure of the financial network and market conditions on financial stability in the European banking system. We capture two channels of financial contagion. The first channel concerns direct interconnectedness, via a network of interbank loans, banks’ loans to other corporate and retail clients, and securities holdings. The second channel concerns indirect interconnectedness, via overlapping exposures to common asset classes. To assess the impact of contagion, we apply the structural valuation model NEVA, in which common shocks to banks’ external assets are reflected in a consistent way in the market value of banks’ mutual liabilities through the network of obligations. Applying the model to a unique supervisory data set collected by the European Central Bank that covers 26 large banks in the euro area we identify a strongly non-linear relationship between diversification of exposures, shock size, and losses due to interbank contagion. We also demonstrate the potential for contagion effects to amplify first-round stress test results due to interconnectedness. Finally, we provide insights into the potential impact of more diversified versus more domestic portfolio allocation strategies on the propagation of contagion, which are relevant to policy discussions about international risk sharing, for instance, in the context of the EU Capital Market Union.

On the Optimal Control of Interbank Contagion in the Euro Area Banking System

In this paper we present a methodology of model-based calibration of additional capital needed in an interconnected financial system to minimize potential contagion losses. Building on ideas from combinatorial optimization tailored to controlling contagion in case of complete information about an interbank network, we augment the model with three plausible types of fire sale mechanisms. We then demonstrate the power of the methodology on the euro area banking system based on a network of 373 banks. On the basis of an exogenous shock leading to defaults of some banks in the network, we find that the contagion losses and the policy authority's ability to control them depend on the assumed fire sale mechanism and the fiscal budget constraint that may or may not restrain the policy authorities from infusing money to halt the contagion. The modelling framework could be used both as a crisis management tool to help inform decisions on capital/liquidity infusions in the context of resolutions and precautionary recapitalisations or as a crisis prevention tool to help calibrate capital buffer requirements to address systemic risks due to interconnectedness. JEL Classification: C61, D85, G01, G18, G21, G28, L14

FinTech and the COVID-19 Pandemic: Evidence from Electronic Payment Systems

This paper investigates the effects of the COVID-19 pandemic on financial institutions and consumers' adoption of FinTech in payments. We find [1] The COVID-19 pandemic has accelerated the adoption of FinTech platforms in payments. We document an approximate increase of 54% in mobile banking transactions, an increase of 19.56% in Mobile agents and an increase of 14.56% in Mobile banking accounts [2] The use of all electronic payment cards has significantly declined during the pandemic. [3] The pandemic has magnified interbank contagion and liquidity risks and has reduced both domestic and international electronic fund transfers via both the Real-Gross Settlement system (RTGS) and via Clearing house (ACH). Overall, our results shows that FinTech not only alleviated the negative impact of the COVID-19 pandemic during Q1 but that the pandemic has also accelerated consumers' adoption of FinTech platforms and digital onboarding specially in Q3 and Q4.

Bail-in and interbank contagion risk: an application of FSQCA methodology

This work uses fuzzy set theory and qualitative comparative analysis (QCA) to determine the causal configurations leading to interbank contagion in a resolution event. This study pioneers the introduction of fsQCA methodology in banking crisis analyses. The event providing the necessary data for this study is the resolution of the Spanish bank Banco Popular. We develop sufficient and necessary condition analyses to find the key metrics that lead to interbank contagion. The results demonstrate that weak solvency metrics, low asset quality and belonging to the same country where the resolution has been triggered tend to lead to higher contagion.

Typology of Stress Testing: Microprudential vs. Macroprudential Stress Testing of Risk Exposures

The most recent global credit mishap of 2008, the worst financial catastrophe of the 21st century, succeeded the Great Depression of the 1930s as the worst event of all times, and used in stress testing under severely adverse scenario analysis. Rather promoting financial stability, the Basel standards (Basel I, II, and III), micro and macroprudential stress tests, and the IMF’s Financial Sector Assessment Program have gravely failed to strengthen the global banking resilience. Furthermore, early stress tests conducted by individual banks, supervisors, multilateral standard-setting agencies, and central banks failed to capture important risks in the trading book such as pipeline, securitization, short-term funding liquidity, interbank contagion and counterparty default. Inabilities of financial authorities and governments in advanced nations to prevent recurrence of banking crises, alleviate their costs to economies, and safeguard global financial stability created a situation where over $30 trillion dollars of wealth globally just vanished, pushing millions of people into poverty. Prolonged financial stability serves the world well but not the United States; for instance, the Fed’s misguided and unjustified tight-money policy in the late 1920s ended stability in the U.S. and the world, triggered the 1929 stock market crash and the subsequent Great Depression – the worst financial catastrophe of the 20th century. Stability during the Clinton administration ended when President Clinton repealed the Glass-Steagall Act of 1933 (that barred commercial banks from investment banking activities) by signing into law the 1999 Gramm-Leach Bliley Act that allowed banks pretty much do anything they wanted. Consequently, financial innovation (mortgage-backed instruments), lax credit environment coupled with predatory lending practices resulted in mortgage debacle in 2006 and subsequently the global financial crisis (GFC) in 2008. A decade has passed since that unprecedented financial accident, the Trump administration wants to reverse the bank regulation rules that were put in place following the GFC. It looks like we are drawn into another perfect storm, but this time, pulling out of it may not be all that easy; in the process, the world may come at the brink of another Great War.

Optimal Stress Tests in Financial Networks

I study the ex ante information design on a financial network to maximize its stability, where banks' endogenous default outcomes are determined by a fixed point payment problem that accounts for both project qualities and interbank contagion. In addition to the cross-state risk sharing in single-bank stress test models, the system-level design highlights the novel cross-bank risk sharing—the banks that are passed together as a group become healthier due to reduced counterparty risk, but at a cost of reporting the same information despite their independent fundamentals. When expected bank profitability is high, or counterparty exposure is large, the optimal policy is relatively less discriminatory across banks. For specific network structures, I find: 1) in a ring network, banks at least a specific distance away from the nearest shock may pass, at states where shocks are connected on adjacent banks; 2) under the optimal policy, a connected structure is more efficient in reducing counterparty risk, but a sparse structure is more efficient in quarantining shocks; so an interconnected structure is not necessarily more stable; 3) typically more connected banks receive preferred treatment.

Default cascades and systemic risk on different interbank network topologies

This paper examines the relationship between the topology of interbank networks and their ability to propagate localized, idiosyncratic shocks across the banking sector via banks’ interbank claims on one another. We begin by creating a wide variety of networks and heterogeneous balance sheet structures using a generative algorithm capable of replicating key characteristics of real-world interbank networks. On each network, we run a standard financial contagion model with cascading defaults. Our modeling framework differentiates between random and targeted shocks of varying magnitude. Interbank contagion comprises a direct channel via banks’ cross-exposures and an indirect channel due to liquidity effects and external asset fire sales. Last, we develop an empirical model to test which global features of the network, aggregate banking sector balance sheet and shock properties drive contagion dynamics. Our results show a strong stabilizing role played by system leverage across all specifications. Among the global network measures, average path length, network density and assortativity are shown to drive the number of failures in a manner consistent with their definition. Similarly, the centralities of the shocked banks (across all definitions) play a significant role in the default cascade. By contrast, allowing for liquidity effects diminishes the explanatory power of the network on both global and local scales. However, taking the change in asset price (primarily driven by liquidity effects) as the dependent variable reestablishes the link between the network structure and the extent of financial contagion.

Interconnected Banks and Systemically Important Exposures

We study the interplay between two channels of interconnectedness in the banking system. The first one is a direct interconnectedness, via a network of interbank loans, banks' loans to other corporate and retail clients, and securities holdings. The second channel is an indirect interconnectedness, via exposures to common asset classes. To this end, we analyze a unique supervisory data set collected by the European Central Bank that covers 26 large banks in the euro area. To assess the impact of contagion, we apply a structural valuation model NEVA (Barucca et al., 2016a), in which common shocks to banks' external assets are reflected in a consistent way in the market value of banks' mutual liabilities through the network of obligations. We identify a strongly non-linear relationship between diversification of exposures, shock size, and losses due to interbank contagion. Moreover, the most systemically important sectors tend to be the households and the financial sectors of larger countries because of their size and position in the financial network. Finally, we provide policy insights into the potential impact of more diversified versus more domestic portfolio allocation strategies on the propagation of contagion, which are relevant to the policy discussion on the European Capital Market Union.

Simulating financial contagion dynamics in random interbank networks

The purpose of this study is to assess the resilience of financial systems to exogenous shocks using techniques drawn from the theory of complex networks. We investigate by means of Monte Carlo simulations the fragility of several network topologies using a simple default model of contagion applied on interbank networks of varying sizes. We trigger a series of banking crises by exogenously failing each bank in the system and observe the propagation mechanisms that take effect within the system under different scenarios. Finally, we add to the existing literature by analyzing the interplay of several crucial drivers of interbank contagion, such as network topology, leverage, interconnectedness, heterogeneity and homogeneity across bank sizes and interbank exposures.

Cross-border interbank contagion in the European banking sector

This paper studies the scope for cross-border contagion in the European banking sector using true bilateral exposure data. Using a model of sequential solvency and liquidity cascades in networks, we analyze geographical patterns of loss propagation from 2008 to 2012. We study the distribution of contagion outcomes after a common shock and an exogenous bank default over simulated networks of actual long- and short-term claims. We exploit a novel and unique dataset of money market transactions estimated from TARGET2 payments data. Our results suggest the evidence for cross-border contagion with evolving over the years geographical patterns and decreasing potential for contagion. Losses due to defaults of domestic counterparties remain on average more important. Furthermore, our results underline an important effect of the underlying network structure on the propagation of losses. Notably, an econometric analysis shows that a denser network of long-term commitments with a shorter average path is more prone to contagion, while higher clustering (more triangles) in short-term networks reduces network fragility mostly due to better liquidity sharing.

Inter Bank Contagion in US

Inter Bank Contagion in US

Simulating Financial Contagion Dynamics in Random Interbank Networks

The purpose of this study is to assess the resilience of financial systems to exogenous shocks using techniques drawn from the theory of complex networks. We investigate by means of Monte Carlo simulations the fragility of several network topologies using a simple default model of contagion applied on interbank networks of varying sizes. We trigger a series of banking crises by exogenously failing each bank in the system and observe the propagation mechanisms that take effect within the system under different scenarios. Finally, we add to the existing literature by analyzing the interplay of several crucial drivers of interbank contagion, such as network topology, leverage, interconnectedness, heterogeneity and homogeneity across bank sizes and interbank exposures.

Systemic risk, maximum entropy and interbank contagion

We discuss the systemic risk implied by the interbank exposures reconstructed with the maximum entropy (ME) method. The ME method severely underestimates the risk of interbank contagion by assuming a fully connected network, while in reality the structure of the interbank network is sparsely connected. Here, we formulate an algorithm for sparse network reconstruction, and we show numerically that it provides a more reliable estimation of the systemic risk.