The default contagion of contingent convertible bonds in financial network

This paper investigates the impact of contingent convertible (CoCo) bonds on systemic risk using Eisenberg-Noe’s financial network method, in which the network is linked by debt relationships. As an efficient method for addressing the problem of “too big to fail,” CoCo bonds have received widespread attention, particularly because the trigger for CoCo bonds is a systemic risk event. Thus, the impact of CoCo bonds on systemic risk needs to be addressed. To solve this problem, we adopt default contagion and loss amplification due to network linkage to measure systemic risk, from which we can ascertain the potential impact on it of CoCo bonds. The results show that CoCo bonds enhance the spillover effect of the issuer’s default; meanwhile, sufficient CoCo bonds partly offset the impact of default contagion from other banks. Furthermore, CoCo bonds enhance the amplification effect of loss due to network linkage, but the amplification effect diminishes after the bankruptcy cost is considered. Finally, the numerical test provides some insight into how the issuance of writedown (WD) bonds influences commercial banks in China. Our study not only offers suggestions to the regulators of CoCo bonds but also contributes to related studies.     

Interconnectedness and systemic risk in the US CDS market

This study assesses systemic risk in the US credit default swap (CDS) market. First, this study estimates the bilateral exposures matrix using aggregate fair value data and theoretically analyze interconnectedness in the US CDS network using various network measures. Second, this study theoretically analyzes the contagious defaults. The default analysis shows the theoretical occurrence of many stand-alone defaults and one contagious default via the CDS network during the global financial crisis. A stress test based on a hypothetical severe stress scenario predicts almost no future contagious defaults. Thus, risk contagion via the CDS network is unlikely.     

Systemic liquidity contagion in the European interbank market

Systemic liquidity risk, defined by the International Monetary Fund as “the risk of simultaneous liquidity difficulties at multiple financial institutions,” is a key topic in financial stability studies and macroprudential policy-making. In this context, the complex web of interconnections of the interbank market plays the crucial role of allowing funding liquidity shortages to propagate between financial institutions. Here, we introduce a simple yet effective model of the interbank market in which liquidity shortages propagate through an epidemic-like contagion mechanism on the network of interbank loans. The model is defined by using aggregate balance sheet information of European banks, and it exploits country and bank-specific risk features to account for the heterogeneity of financial institutions. Moreover, in order to obtain the European-wide topology of the interbank network, we define a block reconstruction method based on the exchange flows between the various countries. We show that the proposed contagion model is able to estimate systemic liquidity risk across different years and countries. Results suggest that our effective contagion approach can be successfully used as a viable alternative to more realistic but complicated models, which not only require more specific balance sheet variables with high time resolution but also need assumptions on how banks respond to liquidity shocks.

     

Networks and market-based measures of systemic risk: the European banking system in the aftermath of the financial crisis

This article investigates the behaviour of the European banking system during the financial crises that occurred in the last decades. Among the various approaches for measuring systemic risk, we consider network analysis, which describes the linkages among financial institutions and their whole structure. We construct a time-varying network of the European banking system. Banks are linked to form a global interconnected system and they mutually influence one another in terms of risk. We model their reciprocal influence via a weighted and directed network, in which weights are related to risk measures that are based on equity returns. Then, we apply two network indicators to investigate the prominence of a bank in spreading and receiving risk from the others. The results enable us to capture many features of the banking system while identifying the global systemically important banks. Moreover, the results of the analysis over time show how interconnections change over periods that are characterized by various economic scenarios.

     

Topological properties of reconstructed credit networks and banking systemic risk

Financial bipartite networks provide channels for contagion risks and their topological properties determine financial stability. We enrich the bipartite network reconstruction methods proposed by Ramadiah et al. (2020) and extend them to the Chinese banking system. By comparing the reproducibility of the real credit market and the corresponding systemic risk, the impact of topological properties for different reconstructed bipartite networks on financial stability is analyzed. The empirical evidence shows that network reconstruction methods based on maximum entropy ensembles capture more properties in the real credit network. It also highlights that the different systemic risk level is mainly contributed by the topological properties based on common exposures. These analyses for topological properties provide regulatory insights for systemic risk prevention. It shows that reducing credit similarity across banks while increasing credit diversification in different sectors helps to control systemic risk. The results imply the possibility of increasing financial stability through the macro-regulation of the credit market structure.     

Overlapping portfolios,contagion, and financial stability

We study the problem of interacting channels of contagion in financial networks. The first channel of contagion is counterparty failure risk; this is captured empirically using data for the Austrian interbank network. The second channel of contagion is overlapping portfolio exposures; this is studied using a stylized model. We perform stress tests according to different protocols. For the parameters we study neither channel of contagion results in large effects on its own. In contrast, when both channels are active at once, bankruptcies are much more common and have large systemic effects.     

Portfolio diversification and systemic risk in interbank networks

This paper contributes to a growing literature on the ambiguous effects of risk diversification. In our model, banks hold claims on each other’s liabilities that are marked-to-market on the individual financial leverage of the obligor. The probability of systemic default is determined using a passage-problem approach in a network context and banks are able to internalize the network externalities of contagion through their holdings. Banks do not internalize the social costs to the real economy of a systemic default of the banking system. We investigate the optimal diversification strategy of banks in the face of opposite and persistent economic trends that are ex-ante unknown to banks. We find that the optimal level of risk diversification may be interior or extremal depending on banks exposure the external assets and that a tension arises whereby individual incentives favor a banking system that is over-diversified with respect to the level of diversification that is desirable in the social optimum.     

Spatial spillover effects and risk contagion around G20 stock markets based on volatility network

Employing the spatial econometric model as well as the complex network theory, this study investigates the spatial spillovers of volatility among G20 stock markets and explores the influential factors of financial risk. To achieve this objective, we use GARCH-BEKK model to construct the volatility network of G20 stock markets, and calculate the Bonacich centrality to capture the most active and influential nodes. Finally, we innovatively use the volatility network matrix as spatial weight matrix and establish spatial Durbin model to measure the direct and spatial spillover effects. We highlight several key observations: there are significant spatial spillover effects in global stock markets; volatility spillover network exists aggregation effects, hierarchical structure and dynamic evolution features; the risk contagion capability of traditional financial power countries falls, while that of “financial small countries” rises; stock market volatility, government debt and inflation are positively correlated with systemic risk, while current account and macroeconomic performance are negatively correlated; the indirect spillover effects of all explanatory variables on systemic risk are greater than the direct spillover effects.     

Stock market linkages and financial contagion: A cobreaking analysis

Financial crises have shown that dramatic movements in one financial market can have a powerful impact on other markets. This paper proposes to use cobreaking to model comovements between stock markets during crises and to test for contagion. We find evidence of cobreaking between developed stock markets. In emerging stock markets, the evidence of cobreaking is mainly due to the non-financial event of the World Trade Center terrorist attacks in 2001. We find evidence of short-term linkages during times of crisis but not contagion. These short-term linkages have important implications for investors, risk managers and regulators.     

中国影子银行部门系统性风险的形成、影响与应对

本文分析了影子银行风险传染机制及其影响,在违约风险基于会计账户传染的马尔科夫过程假设下,运用投入产出法构建影子银行系统性风险测度模型,以2007-2012年中国影子银行业务数据进行检验,结果显示：信托公司部门是主要的风险源,银行部门是系统性风险最主要的承担者,观测期内影子银行部门系统性风险整体呈现上升趋势。防控系统性风险应从影子银行业务风险隔离机制、资本与杠杆率监管、信息透明度、宏观审慎框架和风险应急机制等建设着手。     

Analysis of the cross-region risk contagion effect in stock market based on volatility spillover networks: Evidence from China

This article uses the stock market regional indexes of 31 provinces (include Province-level municipalities and Minority Autonomous Regions) in mainland China as a sample, and constructs an inter-regional volatility spillover network of China’s stock market based on the GARCH-BEKK model. Through network centrality analysis, Diebold and Yilmaz's spillover index method and block model analysis, we comprehensively analyze the risk contagion effect among different regions in China’s stock market. The empirical results show that: (i) The risk contagion intensity (risk reception intensity) in various regions of China’s stock market has a typical “core-periphery” distribution characteristic due to regions’ different levels of economic development. (ii) There are obvious risk spillover effect in China’s stock market, among which the economically developed regions along the southeastern coast of China, such as Beijing, Shanghai, Zhejiang and Jiangsu, are the main risk transmitters, while the economically undeveloped regions in the Midwest of China, such as Xinjiang, Xizang, Gansu, Nei Menggu and Qinghai are the main risk receivers. (iii) Each region is divided into 4 blocks according to their respective roles in the risk spillover process in China’s stock market. Block 1 that is composed of the economically underdeveloped regions in the Midwest is the “main benefit block”, it acts as a “receiver”. Block 2 that is composed of regions with strong economic growth vitality in the Midwest is a “Bilateral spillover block”, it both plays the role of “receiver” and “transmitter”. Block 3 that is composed of developed regions along the southeast coast, it acts as a “transmitter”; Block 4 that is composed of the relatively fast-growing regions in the Southwest is the “brokers block”, it serves as a “bridge”. The results of this article can provide some reference for investors in financial institutions and decision makers in financial regulators.     

Contagion effect of systemic risk among industry sectors in China’s stock market

This paper constructs a tail event driven network to investigate the interdependence of tail risks among industries in the Chinese stock market from 2014 to 2019, and identifies systemically important industries that have made significant contributions to risk contagion by systemic risk decomposition technique. The empirical results suggest strong linkages among industry sectors. The risk profiles of certain industries under close supply–demand relationships are positively correlated, whereas the financial industry, particularly banking, proves to be the principal risk diversifier in the network, with the household appliance, food and drink industries performing likewise an important role in risk diversification. Based on the TENQR model, further study on additional information provided by the industrial chain structure demonstrates that the upstream industry dominates the spread of risks under extreme market conditions. Our findings are of constructive significance to the anticipative introduction of corresponding policies by regulatory authorities, and are also instructive to the investors’ allocation of assets.     

Incentivizing resilience in financial networks

When banks extend loans to each other, they generate a negative externality in the form of systemic risk. They create a network of interbank exposures by which they expose other banks to potential insolvency cascades. In this paper, we show how a regulator can use information about the financial network to devise a transaction-specific tax based on a network centrality measure that captures systemic importance. Since different transactions have different impact on creating systemic risk, they are taxed differently. We call this tax a systemic risk tax (SRT). We use an equilibrium concept inspired by the matching markets literature to show analytically that this SRT induces a unique equilibrium matching of lenders and borrowers that is systemic-risk efficient, i.e. it minimizes systemic risk given a certain transaction volume. On the other hand, we show that without this SRT multiple equilibrium matchings exist, which are generally inefficient. This allows the regulator to effectively stimulate a ‘rewiring’ of the equilibrium interbank network so as to make it more resilient to insolvency cascades, without sacrificing transaction volume. Moreover, we show that a standard financial transaction tax (e.g. a Tobin-like tax) has no impact on reshaping the equilibrium financial network because it taxes all transactions indiscriminately. A Tobin-like tax is indeed shown to have a limited effect on reducing systemic risk while it decreases transaction volume.     

A Primer on Financial Contagion

Abstract.  This paper presents a theoretical framework to highlight possible channels for the international transmission of financial shocks. We first review the different definitions and measures of contagion adopted by the literature. We then use a simple multi-country asset pricing model to classify the main elements of the current debate on contagion and provide a stylized account of how a crisis in one country can spread to the world economy. In particular, the model shows how crises can be transmitted across countries, without assuming ad hoc portfolio management rules or market imperfections. Finally, tracking our classification, we survey the results of the empirical literature on contagion.     

Network VAR models to measure financial contagion

Financial contagion among countries can arise from different channels, the most important of which are financial markets and bank lending. The paper aims to build an econometric network approach to understand the extent to which contagion spillovers (from one country to another) aris from financial markets, from bank lending, or from both. To achieve this aim we consider a model specification strategy which combines Vector Autoregressive models with network models. The paper contributes to the contagion literature with a model that can consider bank exposures and financial market prices, jointly and not only separately. From an empirical viewpoint, our results show that both bilateral exposures and market prices act as contagion channels in the transmission of shocks arising from a country to other countries.     

Identifying contagion

Identifying contagion effects during periods of financial crisis is known to be complicated by the changing volatility of asset returns during periods of stress. To untangle this we propose a GARCH (generalized autoregressive conditional heteroskedasticity) common features approach, where systemic risk emerges from a common factor source (or indeed multiple factor sources) with contagion evident through possible changes in the factor loadings relating to the common factor(s). Within a portfolio mimicking factor framework this can be identified using moment conditions. We use this framework to identify contagion in three illustrations involving both single and multiple factor specifications: to the Asian currency markets in 1997–1998, to US sectoral equity indices in 2007–2009 and to the CDS (credit default swap) market during the European sovereign debt crisis of 2010–2013. The results reveal the extent to which contagion effects may be masked by not accounting for the sources of changed volatility apparent in simple measures such as correlation.     

Robustness of banking networks to idiosyncratic and systemic shocks: a network-based approach

We propose a network-based structural model of credit risk to demonstrate how idiosyncratic and systemic shocks propagate across the banking system and evaluate the costs. The banking system is built as a network of heterogeneous banks which are connected with one another. In such a system, single credit events propagate through the interbank market from debtors to creditors and across the system. The shock is imposed as an unexpected event. We demonstrate that while idiosyncratic shocks cannot substantially disturb the banking system, a systemic shock of even a moderate magnitude can be highly detrimental. Such shock includes a huge contagious potential. We demonstrate that the costs of the shock are largely determined by the extent of contagion and range from negligible to catastrophic. The results imply that a severe crisis has to be initiated by a systemic shock of at least moderate magnitude. Capital ratio and the bank size are two additional factors of the banking system stability. Finally, credit risk analysis is sensitive to the network topology and exhibits a profound nonlinear characteristic.     

An empirical study of the Mexican banking system’s network and its implications for systemic risk

With the aim to measure and monitor systemic risk, we present some topological metrics for the interbank exposures and the payments system networks. The evolution of such networks is analyzed, we draw important conclusions from the systemic risk's perspective and propose a measure of interconnectedness. Additionally, we suggest non-topological measures to describe individual behavior of banks in both networks. The main findings of this paper are: the structures of the payments and exposures networks are different (in terms of connectivity); the topology of the exposures network changed after the collapse of Lehman Brothers, whereas the structure of the payments network does not; the proposed measure of interconnectedness can be used to determine the importance of a bank in terms of connectivity. Finally, we found that interconnectedness of a bank is not necessarily related with its assets size but it is linked to the contagion it might cause.     

Multiscale financial risk contagion between international stock markets: Evidence from EMD-Copula-CoVaR analysis

Considering the frequency domain and nonlinear characteristics of financial risks, we measure the multiscale financial risk contagion by constructing EMD-Copula-CoVaR models. Using a sample composed of nine international stock markets from January 4, 1999, to May 13, 2021, the empirical study reveals that: (1) EMD-Copula-CoVaR models can effectively measure the multiscale financial risk contagion, and the financial risk contagion is significant at all time scales; (2) The high-frequency component is the major contributor of financial risk contagion; meanwhile, the low-frequency component is the smallest among all time scale components; (3) The risk export of the US financial market to other markets, except the UK under the original and medium-frequency component, is higher than that it receives; and (4) Even though the magnitude of overall financial risk contagion is similar for the COVID-19 pandemic, Subprime Crises, 9/11 terrorist attack and other crises, the relative importance of different frequency components is heterogeneous. Therefore, the countermeasures of risk contagion should be designed according to its multiscale characteristics.     

Systemic risk of Chinese financial institutions and asset price bubbles

Although there has not been a large-scale systemic crisis in China, high-risk financial events have occurred continuously in recent years. This research thus creatively analyzes the determinants of systemic risk for Chinese financial institutions from the view of asset price bubbles. First, we identify bubbles in the China stock and real estate markets on the basis of the generalized sup Augmented Dickey-Fuller (GSADF) model and explain the reasons for bubble formations according to the stage of China's economic development and policies implementation. At this stage, considering the differences in economic development levels of different cities, the real estate bubbles in the first, second and third tier cities and the whole country were innovatively identified. Second, on the basis of the DCG-GARCH-CoVaR model to measure the systemic risk of listed financial institutions in China and to classify institutions, the results show that the main source of such risk is the banking sector. Furthermore, by constructing regression models, stock market bubbles and real estate bubbles both positively correlate with systemic risk throughout the sample period. Meanwhile, the impact of bubbles on the systemic risk of different types of financial institutions was taken into account so that regulators prioritized different types of institutions with different characteristics when faced with decisions. Finally, we provide macro-prudential policy advice to regulators in order to weaken the impact of bubbles on financial stability to avoid systemic crises.