How likely is contagion in financial networks?

Interconnections among financial institutions create potential channels for contagion and amplification of shocks to the financial system. We estimate the extent to which interconnections increase expected losses and defaults under a wide range of shock distributions. In contrast to most work on financial networks, we assume only minimal information about network structure and rely instead on information about the individual institutions that are the nodes of the network. The key node-level quantities are asset size, leverage, and a financial connectivity measure given by the fraction of a financial institution’s liabilities held by other financial institutions. We combine these measures to derive explicit bounds on the potential magnitude of network effects on contagion and loss amplification. Spillover effects are most significant when node sizes are heterogeneous and the originating node is highly leveraged and has high financial connectivity. Our results also highlight the importance of mechanisms that go beyond simple spillover effects to magnify shocks; these include bankruptcy costs, and mark-to-market losses resulting from credit quality deterioration or a loss of confidence. We illustrate the results with data on the European banking system.     

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.     

基于房地产市场的我国系统性金融风险测度与预警研究

本文阐释了基于房地产市场的系统性金融风险形成机制,据此建立了分阶段、跨部门的房地产市场的系统性金融风险网络模型,并运用2006-2017年16家上市银行数据,分析和测度了我国房价大幅下跌所引发的系统性金融风险水平和结构,构建了基于房地产市场的系统性金融风险预警指标并进行测算。研究发现:在房价下跌30%的压力情景下,我国金融体系的潜在总损失总体呈级数式上升,年均增长22.70%;基于房地产市场的系统性金融风险值(SR)呈现先上升后波动下降的总体趋势;系统性金融风险(SR)的脆弱性指标(FLI)整体呈现波浪式振荡变化,且与房地产贷款/权益整体呈反向变动,系统性金融风险(SR)的传染性指标(CTI)在2012-2017年呈持续下降趋势,且与金融市场压力指数、金融机构间资产占总资产比重呈现出高度的一致性变化趋势。最后,基于房地产市场的系统性金融风险预警指标(SRWI)值呈收敛式振荡走势,表明基于房地产市场的系统性金融风险总体可控且呈收敛式下降。     

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.     

基于房地产市场的我国系统性金融风险测度与预警研究

本文阐释了基于房地产市场的系统性金融风险形成机制,据此建立了分阶段、跨部门的房地产市场的系统性金融风险网络模型,并运用2006-2017年16家上市银行数据,分析和测度了我国房价大幅下跌所引发的系统性金融风险水平和结构,构建了基于房地产市场的系统性金融风险预警指标并进行测算。研究发现:在房价下跌30%的压力情景下,我国金融体系的潜在总损失总体呈级数式上升,年均增长22.70%;基于房地产市场的系统性金融风险值(SR)呈现先上升后波动下降的总体趋势;系统性金融风险(SR)的脆弱性指标(FLI)整体呈现波浪式振荡变化,且与房地产贷款/权益整体呈反向变动,系统性金融风险(SR)的传染性指标(CTI)在2012-2017年呈持续下降趋势,且与金融市场压力指数、金融机构间资产占总资产比重呈现出高度的一致性变化趋势。最后,基于房地产市场的系统性金融风险预警指标(SRWI)值呈收敛式振荡走势,表明基于房地产市场的系统性金融风险总体可控且呈收敛式下降。     

Uncertainty in an Interconnected Financial System,Contagion, and Market Freezes

This paper studies contagion and market freezes caused by uncertainty in financial network structures. Our model demonstrates that in a financial system where financial institutions are interconnected, a negative shock to an individual financial institution could spread to other institutions, causing market freezes because of creditors' uncertainty about the financial network structure. Our model also reveals that when both a large creditor and a continuum of small creditors are present, the size of the large creditor will affect the severity of market freezes substantially. Moreover, our model is used to examine central bank policies to alleviate market freezes.     

Interconnected multilayer networks: Quantifying connectedness among global stock and foreign exchange markets

This paper proposes a novel interconnected multilayer network framework based on variance decomposition and block aggregation technique, which can be further served as a tool of linking and measuring cross-market and within-market contagion. We apply it to quantifying connectedness among global stock and foreign exchange (forex) markets, and demonstrate that measuring volatility spillovers of both stock and forex markets simultaneously could support a more comprehensive view for financial risk contagion. We find that (i) stock markets transmit the larger spillovers to forex markets, (ii) the French stock market is the largest risk transmitter in multilayer networks, while some Asian stock markets and most forex markets are net risk receivers, and (iii) interconnected multilayer networks could signal the financial instability during the global financial crisis and the COVID-19 crisis. Our work provides a new perspective and method for studying the cross-market risk contagion.     

Tail risk contagion between international financial markets during COVID-19 pandemic

In this paper, we combine the time-varying financial network model and FARM-selection approach to analyze the tail risk contagion between international financial market during the COVID-19 epidemic. Since the tail risk acts as a global transmission channel, we use the sample of 19 international financial markets to explore the contagion of tail risk during the epidemic. We find that the COVID-19 epidemic increases the number of contagion channels in the international financial system. The clustering level of the financial system has a significant growth during the COVID-19 pandemic, and the number of risk drivers is also larger than risk takers. The key financial market of each international financial network is related to the epidemic country. We also consider the tail risk contagion in local financial markets and find that the COVID-19 pandemic has an important influence on the tail risk contagions in local network systems     

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.     

Multilayer networks in the frequency domain: Measuring extreme risk connectedness of Chinese financial institutions

We propose multilayer networks in the frequency domain, including the short-term, medium-term, and long-term layers, to investigate the extreme risk connectedness among financial institutions. Using the conditional autoregressive value at risk (CAViaR) tool to measure the extreme risk of financial institutions, we construct extreme risk networks and inter-sector extreme risk networks of 36 Chinese financial institutions through the proposed approach. We observe that the extreme risk connectedness across financial institutions is heterogeneous in the short-, medium-, and long-term. In general, the long-term connectedness among financial institutions rises sharply during times of financial stress, such as the 2015 Chinese stock market turbulence and the 2020 COVID-19 pandemic. Moreover, we note that the insurers are key players in driving the inter-sector extreme risk networks, because the inter-sector systemic importance of insurance institutions is dominant. Finally, our conclusions provide valuable information for regulators to prevent systemic risk.     

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.     

What Is Systemic Risk?

The traditional view of risk in a financial system is that it is the summation of individual risks within the system. However, the financial crisis that started in 2007 has driven home that this view of risk is inadequate. It is the interactions of financial institutions and markets that determine the systemic risks that drive financial crises. We identify four types of systemic risk. These are (i) panics—banking crises due to multiple equilibria; (ii) banking crises due to asset price falls; (iii) contagion; and (iv) foreign exchange mismatches in the banking system.     

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.     

Tail dependence and indicators of systemic risk for large US depositories

In this study, we investigate the extreme loss tail dependence between stock returns of large US depository institutions. We find that stock returns exhibit strong loss dependence even in their limiting joint extremes. Motivated by this result, we derive extremal dependence-based systemic risk indicators. The proposed systemic risk indicators reflect downturns in the US financial industry very well. We also develop a set of firm-level average extremal dependence measures. We show that these firm-level measures could have been used to identify the firms that were more vulnerable to the 2007–2008 financial crisis. Additionally, we explore the performance of selected systemic risk indicators in predicting the crisis performance of large US depository institutions and find that the average stock return correlations are also good predictors of crisis period returns. Finally, we identify factors predictive of extremal dependence for the US depository institutions in a panel regression setting. Strength of extremal dependence increases with asset size and similarity of financial fundamentals. On the other hand, strength of extremal dependence decreases with capitalization, liquidity, funding stability and asset quality. We believe the proposed indicators have the potential to inform the prudential supervision of systemic risk.     

基于动态因子Copula模型的我国银行系统性风险度量

本文基于2007年至2019年我国14家上市银行的股票收益率,构建偏态t-分布动态因子Copula模型,利用时变荷载因子刻画单家银行与整个系统的相关性,计算联合风险概率作为系统性风险整体水平的度量,基于关联性视角提出了新的单家机构系统脆弱性和系统重要性度量指标——系统脆弱性程度和系统重要性程度。该方法充分考虑了银行个体差异性和系统的内在关联性以及收益率的厚尾性和非对称性,从而能够捕捉到更多的信息且兼具时效性。研究表明:银行机构在风险聚集时期相关程度更大,联合风险概率能够准确识别出系统性风险事件且在我国推行宏观审慎评估体系以后有明显降低;整体而言,大型商业银行系统重要性水平最高,同时风险抗压能力也最强;本文使用的度量方法降低了数据获取成本且更具时效性,有助于为宏观审慎差异化监管工作提供借鉴和参考。     

Systemic Risk in the Chinese Shadow Banking System: A Sector-Level Perspective

We propose to measure the systemic risk in the shadow banking sector. Instead of testing how many institutions will fail due to the initial breakdown of one institution as extant network models do, we associate the systemic risk of one shadow banking sector with the total amount of unexpected losses it might generate both directly and indirectly. Our model focuses on balance sheet contagion and applies a loop algorithm to risk transfer. The result shows that trust companies were the main culprit of financial instability and commercial banks assumed the main risks over 2007–12 in the Chinese shadow banking system.     

宏观审慎与货币政策双支柱框架研究——基于系统性风险视角

本轮国际金融危机之后,建立“宏观审慎政策专门盯住金融稳定目标,货币政策主要关注经济稳定目标”的双支柱成为国际社会的普遍共识。本文基于系统性风险视角,深入剖析系统性风险的累积和实现机制,从时间和空间两个维度梳理宏观审慎政策实现金融稳定的有效性,以及货币政策对系统性风险造成的潜在溢出性。目前从系统性风险的时间维度探讨双支柱政策的研究已较为丰富,可以总结为宏观审慎政策的“逆周期调节”机制和货币政策的“资本缺口”机制。从系统性风险的空间维度探讨双支柱政策的研究,也即对双支柱政策如何作用和改变金融机构内部关联网络的研究正成为研究热点。本文从政策工具和影响机制上对空间维度双支柱政策进行了系统梳理。基于以上分析,本文对双支柱政策的制定提出如下建议:时间维度宏观审慎政策要关注并消除货币政策对时间维度系统性风险的溢出性,同时要加强空间维度宏观审慎政策工具的创新力度。     

Dynamical macroprudential stress testing using network theory

The increasing frequency and scope of financial crises have made global financial stability one of the major concerns of economic policy and decision makers. This has led to the understanding that financial and banking supervision has to be thought of as a systemic task, focusing on the interdependent relations among the institutions. Using network theory, we develop a dynamic model that uses a bipartite network of banks and their assets to analyze the system’s sensitivity to external shocks in individual asset classes and to evaluate the presence of features underlying the system that could lead to contagion. As a case study, we apply the model to stress test the Venezuelan banking system from 1998 to 2013. The introduced model was able to capture monthly changes in the structure of the system and the sensitivity of bank portfolios to different external shock scenarios and to identify systemic vulnerabilities and their time evolution. The model provides new tools for policy makers and supervision agencies to use for macroprudential dynamical stress testing.     

Completeness,interconnectedness and distribution of interbank exposures—a parameterized analysis of the stability of financial networks

This paper assesses the impact of a certain structure of interbank exposures on the stability of a stylized financial system. Given a certain balance sheet structure of financial institutions, a large number of valid matrices of interbank exposures is created by a random generator. Assuming a certain loss given default, domino effects are simulated. The main results are, first, that financial stability depends not only on the completeness and interconnectedness of the network, but also on the distribution of interbank exposures within the system (measured by entropy). Second, looking at random graphs, the sign of the correlation between the degree of equality of the distribution of claims and financial stability depends on the connectivity of the financial system as well as on additional parameters that affect the vulnerability of the system to interbank contagion. Third, the more concentrated the assets are within a money center model, the less stable it is. Fourth, a money center model with asset concentration among core banks is less stable than a random graph with banks of homogeneous size.     

全球股票市场间风险传染的测度、监管及预警

近年来,伴随金融一体化程度的加深,全球各股票市场间风险传染的动态复杂性加剧,其准确测度、高效监管及实时预警已成为优先事项。本研究选取全球21个代表性股票市场作为分析样本,首先基于广义向量自回归模型的滚动估计准确测度其间风险动态传染的高维网络序列,进一步借由矩阵值因子模型来稳健收缩上述序列,以探究其潜在动态核心结构,从而实现高效监管。最后,通过向量自回归模型的预测功能实现对全球股票市场间风险传染的实时预警。研究表明,全球股票市场间风险传染具有时变性,其监管与预警可通过少数与地理区域高度相关的风险区域间的动态传染关系及内部的市场构成来刻画。与此同时,我们发现中国内地等新兴市场的重要地位逐渐凸显。本文研究结论可为有效防范与化解金融风险提供有益参考。