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.     

Research on China's financial systemic risk contagion under jump and heavy-tailed risk

To accurately measure the dynamic characteristics of systemic risk contagion under the impact of extreme financial events, we construct a research framework that analyzes the contagion dynamics of systemic risk under extreme risk impact from the perspectives of both time and space. Based on the macro-jump CCA method, this paper extracts the heterogeneous volatility sequence of financial industries considering the thick tail of the distribution of financial assets returns. Then, the dynamic variation of systemic risk in the financial sectors is characterized from the time dimension. The volatility spillover network method is used to examine the spillover contagion of systemic risk among financial system sectors from the spatial dimension. Empirical studies have found that when considering the risk contagion level, the capital market service sector plays a risk‑leading role, followed by the currency service sector and the insurance sector. The measurement indicators that consider the jump risk and the tail risk have good early warning effects on extreme financial events. Seen from the spatial direction of risk spillover, the real estate sector exhibits the most obvious risk spillover effect on other sectors and can be regarded as the source of systemic risk, which suggests differentiated regulation.     

基于信用链接的银行网络风险传染研究述评

对传染性银行风险的研究一直是重要的问题,本文全面介绍了基于信用链接的银行网络中风险传染研究的最新成果。该领域研究主要是从两个方面来展开,一方面是从经济学的角度对银行网络中风险传染进行研究;另一方面是从经济物理的角度,通过对银行网络结构和银行主体建模,并用仿真的方法研究银行网络中的风险传染问题。本文对目前研究现状存在的不足进行了评述,并进一步指出了未来的研究方向。     

Cross-border interbank networks,banking risk and contagion

Recent events have highlighted the role of cross-border linkages between banking systems in transmitting local developments across national borders. This paper analyzes whether international linkages in interbank markets affect the stability of interconnected banking systems and channel financial distress within a network consisting of banking systems of the main advanced countries for the period 1994–2012. Methodologically, I use a spatial modeling approach to test for spillovers in cross-border interbank markets. The results suggest that foreign exposures in banking play a significant role in channeling banking risk: I find that countries that are linked through foreign borrowing or lending positions to more stable banking systems abroad are significantly affected by positive spillover effects. From a policy point of view, this implies that in stable times, linkages in the banking system can be beneficial, while they have to be taken with caution in times of financial turmoil affecting the whole system.     

SAFE: An early warning system for systemic banking risk

This paper builds on existing microprudential and macroprudential early warning systems (EWSs) to develop a new, hybrid class of models for systemic risk that incorporates the structural characteristics of the financial system and a feedback amplification mechanism. The models explain financial stress using both public and proprietary supervisory data from systemically important institutions, regressing institutional imbalances using an optimal lag method. The Systemic Assessment of Financial Environment (SAFE) EWS monitors microprudential information from the largest bank holding companies to anticipate the buildup of macroeconomic stresses in the financial markets. To mitigate inherent uncertainty, SAFE develops a set of medium-term forecasting specifications that gives policymakers enough time to take ex-ante policy action and a set of short-term forecasting specifications for verification and adjustment of supervisory actions. This paper highlights the application of these models to stress testing and policy.     

Measuring systemic risk in the European banking sector: a copula CoVaR approach

We propose a new methodology based on copula functions to estimate CoVaR, the Value-at-Risk (VaR) of the financial system conditional on an institution being under financial distress. Our Copula CoVaR approach provides simple, closed-form expressions for various definitions of CoVaR for a broad range of copula families and allows the CoVaR of an institution to have time-varying exposure to its VaR. We extend this approach to estimate other ‘co-risk’ measures such as Conditional Expected Shortfall (CoES). We focus on a portfolio of large European banks and examine the existence of common market factors triggering systemic risk episodes. Further, we analyse the extent to which bank-specific characteristics such as size, leverage, and equity beta are associated with institutions' contribution to systemic risk and highlight the importance of liquidity risk at the outset of the financial crisis in summer 2007. Finally, we investigate the link between macroeconomy and systemic risk and find that changes in major macroeconomic variables can contribute significantly to systemic risk.     

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

This 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.     

Risk contagion of COVID-19 in Japanese firms: A network approach

The novel coronavirus disease (COVID-19) is one of the worst pandemics in human history. Our research objective is to assess the contagion effect on Japanese firms and to evaluate the Japanese government's COVID-19 measures during the period from April 7, 2020, to May 25, 2020. We propose a susceptible-infected-recovered-dead model for COVID-19 and derive COVID-19 parameters for Japan. Subsequently, we analyze the effect of COVID-19 on Japanese firms through correlation-based network and credit risk analyses. The main findings are that the Tokyo Stock Price Index moved in the opposite direction of COVID-19 parameters and COVID-19 parameters are almost the only risk factors that impact a firm's credit risk during the period. Finally, we find that the interconnection analysis between the COVID-19 infection network and the financial networks contribute to the existing pandemic risk management knowledge.     

Default contagion and systemic risk in loan guarantee networks

This paper studies systemic risk in the Chinese debt market stemming from inter‐corporate loan guarantees using field data from Zhejiang Province. We apply a weighted and directed network model to analyse the implications for default contagion and systemic risk under different stress testing scenarios. The empirical results indicate that the topology of the loan guarantee network is close to a ‘scale‐free’ structure, which is known to be robust against accidental failures but vulnerable to coordinated attacks. Hence, the network is able to cope with idiosyncratic shocks resulting from single company failures, but can easily suffer from more widespread contagion if a group of systemically important companies are hit by a targeted shock. We further demonstrate that within our sample of small and medium‐sized enterprise (SME) companies, increasing leverage reduces network stability and exacerbates the effects of contagion. More lenient bank lending policies increase the survival rate of sample companies and thereby reduce the losses from default contagion.     

Conditional risk, return and contagion in the banking sector in asia

This paper investigates risk and return in the banking sector in three Asian markets of Taiwan, China and Hong Kong. The study focuses on the risk-return relation in a conditional factor GARCH-M framework that controls for time-series effects. The factor approach is adopted to incorporate intra-industry contagion and an analysis of spillovers between large banks and small banks. Finally, the study provides evidence on these relations before and after the Asian financial crisis of 1997. The results are generally consistent across the markets and with expectations.     

Ranking consistency of systemic risk measures: a simulation-based analysis in a banking network model

Review of Quantitative Finance and Accounting - In a banking network model, I analyse the ranking consistency of common systemic risk measures (SRMs). In contrast to previous studies, this...     

Measuring systemic risk: A risk management approach

This paper proposes a new method to measure and monitor the risk in a banking system. Standard tools that regulators require banks to use for their internal risk management are applied at the level of the banking system to measure the risk of a regulator’s portfolio. Using a sample of international banks from 1988 until 2002, I estimate the dynamics and correlations between bank asset portfolios. To obtain measures for the risk of a regulator’s portfolio, I model the individual liabilities that the regulator has to each bank as contingent claims on the bank’s assets. The portfolio aspect of the regulator’s liability is explicitly considered and the methodology allows a comparison of sub-samples from different countries. Correlations, bank asset volatility, and bank capitalization increase for North American and somewhat for European banks, while Japanese banks face deteriorating capital levels. In the sample period, the North American banking system gains stability while the Japanese banking sector becomes more fragile. The expected future liability of the regulator varies substantially over time and is especially high during the Asian crisis starting in 1997. Further analysis shows that the Japanese banks contribute most to the volatility of the regulator’s liability at that time. Larger and more profitable banks have lower systemic risk and additional equity capital reduces systemic risk only for banks that are constrained by regulatory capital requirements.     

Measuring systemic risk contribution of global stock markets: A dynamic tail risk network approach

Measuring the systemic risk contribution (SRC) of country-level stock markets helps understand the rise of extreme risks in the worldwide stock system to prevent potential financial crises. This paper proposes a novel SRC measurement based on quantifying tail risk propagation's domino effect using ΔCoVaR and the cascading failure network model. While ΔCoVaR captures the tail dependency structure among stock markets, the cascading failure network model captures the nonlinear dynamic characteristics of tail risk contagion to mimic tail risk propagation. As an illustration, we analyze 73 markets' SRCs using a daily closing price dataset from 1990.12.19 to 2020.9.8. The validity test demonstrates that our method outperforms seven classic methods as it helps early warning global financial crises and correlates to many systemic risk determinants, e.g., the market liquidity, leverage, inflation, and fluctuation. The empirical results identify that Southeast European markets have higher SRCs with time-varying and momentum features corresponding to significant financial crisis events. Besides, it needs attention that South American and African markets have displayed increasing risk contributions since 2018. Overall, our results highlight that considering tail risk contagion's dynamic characteristics helps avoid underestimating SRC and supplement a “too cascading impactive to fail” perspective to improve financial crisis prevention.     

Assessing systemic risk using interbank exposures in the global banking system

This paper contributes to the literature on systemic risk by examining the network structure of bilateral exposures in the global banking system. The global interbank market constitutes a major part of the global banking system. The market has a hierarchical network structure, composed of the national or jurisdictional area's local markets and the cross-border interbank market. First, we estimate the bilateral exposures matrix using aggregate financial data on loans and deposits from Bankscope and analyze the interconnectedness in the market using network centrality measures. Subsequently, for the model analysis, we apply the Eisenberg–Noe framework to a multi-period setting. In this framework, bank defaults are classified into stand-alone defaults and contagious defaults. The banks in our sample (i.e., the top 202 banks with more than $50 billion in total assets) comprise a major part of this global banking system. The main findings are as follows: The theoretical network analysis using network centrality measures showed that most of the banks designated as global systemically important banks (G-SIBs) play a central role in the global interbank market. The theoretical default analysis showed a few contagious defaults triggered by the basic defaults during and after the global financial crisis. Our stress test proved that many G-SIBs theoretically caused 1–6 contagious defaults. Our methodology would assist in the development of a monitoring system by the respective supervisory authorities as well as in the implementation of bank-internal stress tests of default contagion.     

Debt-overhang banking crises: Detecting and preventing systemic risk

This paper shows how the debt-overhang distortion on bank lending can generate a self-fulfilling-expectations banking crisis accompanied by a plunge in the value of banks’ assets and a contraction of bank lending and economic activity. Moral hazard in banking adds an additional channel that can generate multiple equilibria, worsen the debt-overhang distortion, and deepen the crisis. Some signals of systemic risk include: high volatility and the presence of two modes in the probability distribution functions of the returns on bank-issued bonds and on portfolios of bank-issued bonds and equities; and high correlation between the returns on bank-issued bonds. Macroprudential regulation should discourage the exposure of banks to the economic and financial cycle by raising the capital requirements for banks with more cyclical assets.     

Systemic risk and dynamics of contagion: a duplex inter-bank network

This paper constructs a duplex banking network formed by credit relationships and information interaction via the banks’ balance sheet to model the structure of systemic risk and investigate the dynamic mechanism of contagion in terms of default and liquidity infection along with the factors that affect the extent of the contagion. We systematically explain the role that duplex banking networks play in different aspects of risk contagion. Through theoretical analysis and simulations, we conclude that asymmetric information interaction would increase the inflexibility of the system, which leads to liquidity shortage and possibly the collapse of the whole market. The weakness of systemic risk in the interior of the complex banking system can be characterized by the partial discount factor using illiquid assets in the information network. By improving the connectedness of the information network of the duplex networks, the spread of contagion can be partially slowed.     

Sovereign risk spillovers: A network approach

Understanding the global financial network for sovereign debt, particularly with a focus on interaction and spillover effects of sovereign risk, has become important for policy makers as they look to protect the stability of their economies. Using high dimensional Vector Autoregression techniques and network simulation on Sovereign Credit Default Swaps (CDS)’ data of 57 countries, we identify that the global sovereign CDS network is fully integrated as there is virtually no country without any connection to at least one specific node in the system. However, each country has a unique attribute in the network, as a risk exporter or importer and/or risk transmitter. Among developed countries, the US (unsurprisingly) holds the dominant position as a risk exporter while Germany is identified as a connecting country that transmits shocks. The most connected countries in the sovereign CDS network belong to the new European Union members. We examine possible drivers of the network relationships observed, in order to better understand the risk transmission process, and find that connections in the sovereign risk network are stronger within regional groups and countries with the same level of economic development. Central and Eastern Europe and Middle East and Africa have more interactive networks than Northern Western Europe, Asia Pacific and Latin America. We also identify that financial volatility and economic policy uncertainty increase the interactions in market-based default risk assessment.     

Measuring financial contagion: A Copula approach

This paper models dependence with switching-parameter copulas to study financial contagion. Using daily returns from five East Asian stock indices during the Asian crisis, and from four Latin American stock indices during the Mexican crisis, it finds evidence of changing dependence during periods of turmoil. Increased tail dependence and asymmetry characterize the Asian countries, while symmetry and tail independence describe the Latin American case. Structural breaks in tail dependence are a dimension of the contagion phenomenon. Therefore, the rejection of the correlation breakdown hypothesis should not be considered, without further investigation, as evidence of a stable dependence structure.     

Early warning systems and systemic banking crises in low income countries: A multinomial logit approach

This paper estimates an early warning system (EWS) for predicting systemic banking crises in a sample of low income countries in Sub-Saharan Africa. Since the average duration of crises in this sample of countries is longer than one year, the predictive performance of standard binomial logit models is likely to be hampered by the so-called crisis duration bias. The bias arises from the decision to either treat crisis years after the onset of a crisis as non-crisis years or remove them altogether from the model. To overcome this potential drawback, we propose a multinomial logit approach, which is shown to improve the predictive power of our EWS compared to the binomial logit model. Our results suggest that crisis events in low income countries are associated with low economic growth, drying up of banking system liquidity and widening of foreign exchange net open positions.     

Measuring systemic risk: A factor-augmented correlated default approach

In this paper, we extend existing correlated default models for measuring systemic risk by proposing a model that incorporates an observable common factor that features conditional heteroscedasticity. The addition of the common factor helps to effectively capture realistic time-varying characteristics in individual asset return volatility as well as return correlations. We apply the model for large US financial institutions. The common factor proves its importance in explaining asset return dynamics and measuring systemic risk. We also apply the model in the context of systemic risk contribution analysis and show its applicability.