Systemic Risk Contagion Research Articles

Assessing the interconnectedness and systemic risk contagion in the Chinese banking network

PurposeIn this paper, the authors use the balance sheet data to investigate the interconnectedness and risk contagion effects in China's banking sector. They firstly study the network structure and centrality of the interbank network. Then, they investigate how and to what extent the credit shock and liquidity shock can lead to the risk propagation in the banking network.Design/methodology/approachReferring to the theoretical framework by Haldane and May (2011), this paper uses the network topology theory to analyze the contagion mechanism of credit shock and liquidity shock. Centrality measures and log-log plot are used to evaluate the interconnectedness of China's banking network.FindingsThe network topology has shown clustering effects of large banks in China's financial network. If the Industrial and Commercial Bank of China (ICBC) is in distress, the credit shock has little impact on the Chinese banking sector. However, the liquidity shock has shown more substantial effects than that of the credit shock. The discount rate and the rollover ratio play significant roles in determining the contagion effects. If the credit shock and liquidity shock coincide, the contagion effects will be amplified.Research limitations/implicationsThe results of this paper reveal the network structure of China's interbank market and the resilience of banking system to the adverse shock. The findings are valuable for regulators to make policies and supervise the systemic important banks.Originality/valueThe balance sheet data of different types of banks are used to construct a bilateral exposure matrix. Based on the matrix, this paper investigates the knock-on effects of credit shock triggered by the debt default in the interbank market, the knock-on effects of liquidity effects, which is featured by “fire sale” of bank assets, and the contagion effects of combined shocks.

Systemic Risk Contribution and Contagion of Industrial Sectors in China: From the Global Financial Crisis to the COVID-19 Pandemic

This paper investigates the risk contribution of 29 industrial sectors to the China stock market by using one-factor with Durante generator copulas (FDG) and component expected shortfall (CES) analyses. Risk contagion between the systemically most important sector and other sectors is examined using a copula-based ∆CoVaR approach. The data cover the 2008 global financial crisis and the beginning of the COVID-19 pandemic. The empirical results show that the banking sector contributed most to systemic risk before and during the global financial crisis. Nonbank finance became equally important in 2020, and the COVID-19 pandemic promoted the position of the computer and pharmaceuticals sectors. The spillover effect diminishes over time, but there remains risk contagion between sectors. The risk spillover trend is consistent with that of systemic risk.

Research on systemic risk contagion of Chinese financial institutions based on GARCH-VMD-Copula-CoVaR model

With the development of China's financial market, the risk contagion effect among financial institutions is increasing and becoming more complicated. Few literatures have explored the risk transmission paths of Chinese financial institutions at different frequencies. In order to make up for the gaps in this research field, variable mode decomposition (VMD) technology is introduced in this paper, combined with the Copula-GARCH model to construct the GARCH-VMD-Copula-CoVaR model, which describes the risk contagion paths of major financial institutions in the Chinese financial market at different frequencies (long-term, medium-term and short-term). The research results show that risk dependence and contagion between financial institutions have the characteristics of bidirectionality, asymmetry and time-varying in all frequency studies, and there are differences in different frequencies.

A Network Evolution Model of Credit Risk Contagion between Banks and Enterprises Based on Agent-Based Model

Credit risk contagion between banks and firms is one of the important triggers of financial crisis, and the credit linkage network is the way of systemic risk contagion triggered by external shocks. Considering the heterogeneity of behavioral rules, learning rules, and interaction rules, this paper constructs a bank-firm credit matching network model based on ABM (agent-based model) model and reinforcement learning algorithm to analyze the interaction behavior and credit risk network contagion mechanism. The results show that (1) macroeconomic cycles are the result of the interaction between banks and enterprises and the interaction of microentities under complex financial conditions; (2) enterprises are heterogeneous and the asset size follows a power-law distribution; (3) the greater the sensitivity of banks and enterprises to market performance, the lower the bank failure rate and enterprise default rate; and (4) shocks to the largest banks and enterprises in terms of assets and entry can all intensify the risk contagion between banks and enterprises. Therefore, the regulation of financial institutions that are “too big to fail” is not sufficient but should be a comprehensive regulation of the banking system.

Non-Traditional Systemic Risk Contagion within the Chinese Banking Industry

Systemic risk contagion is a key issue in the banking sector in maintaining financial system stability. This study is among the first few to use three different distance-to-risk measures to empirically assess the domestic interbank linkages and systemic contagion risk of the Chinese banking industry, by using bivariate dynamic conditional correlation GARCH model on data collected from eight prominent Chinese banks for the period 2006–2018. The results show a relatively high correlation among almost all the banks, suggesting an interconnectedness among the banks. We found evidence that the banking system is exposed to significant domestic contagion risks arising from systemic defaults. Given that Chinese markets deliver weak signals of forthcoming stress in banking sectors, new policy intervention is crucial to resolve the hidden stress in the system. The results have important policy implications and will provide scholars and policymakers further insight into the risk contagion originating from interbank networks.

Contagion risk: cases of Islamic and emerging market banks

PurposeGlobally influential Islamic banks from the Middle East and Southeast Asia carry voluminous correspondence banking with banks from China and India, leading to potential spillover effect of contagion among the banks from these regions. This study aims to investigate the Islamic banks systemic risk contagion with major banks from China and India.Design/methodology/approachHaving the option pricing theory in the backdrop, the authors calculated three different distance to risk measurements (default, insolvency and capital). The authors have included top six listed globally influential Islamic banks, top seven Indian banks and top eight Chinese banks based on their net asset value. They then measured the banks’ extreme shocks based on the extreme value theory by using the logistic regression model. These extreme shocks helped the authors to map the spillover among the selected banks from multiple regions.FindingsThe authors have found strong evidences of directional risk spillover among the banks in this sample. Islamic banks are receiving a significant risk spillover from the other sample banks but transmitting less toward the other banks from India and China. Hence, there is strong one-directional risk contagion toward the Islamic banks in the study sample.Practical implicationsThis research would be particularly useful to the regulators and bankers from emerging and Islamic markets to understand the conniving nature of the crisis by effectively mapping the source, destination and implementation of the shock transmission mechanism of the potential financial contagion.Originality/valueEven though the corresponding banking among the top Islamic banks from the Middle East and Southeast Asian countries, and banks from India and China, is on the rise, the assessment of risk among these banks has been limited. In particular, the authors extended on the extreme value theory to focus on the wider impact of spillover, including significant direction of contagion from non-Islamic banks to Islamic banks.

On systemic risk contagion in the euro area: Evidence from frequency connectedness and the DY approaches

This study analyzes systemic risk contagion across the euro area by employing the Diebold-Yilmaz and the frequency connectedness methodologies with data from January 1, 1999, to January 25, 2021. We use the daily Composite Indicator of Systemic Stress (CISS) series for 11 countries in the euro area developed by Holló et al. (2012) and calculate the overall connectedness between the series by employing two pioneering approaches. Additionally, we estimate the short-, medium-, and long-cycle connectedness of systemic risk over the study period that covers three financial burst periods: the global financial crisis (GFC), the European sovereign debt crisis (ESDC), and the COVID-19 pandemic. Overall, spillover indexes notably rise around well-known financial/geopolitical events over the study period. Finally, we examine the network connectedness of systemic risk spillovers during the GFC, the ESDC, and COVID-19 periods and thereby compare them. Our results highlight an efficient regulatory mechanism to control systemic risk contagion.

Systemic risk contagion within US states

PurposeThis study aims to examine the systemic risk contagion in banks from 15 US states using extreme shocks in their distance to risk.Design/methodology/approachThe authors contemplate a model that inputs co-exceedances in the base US states’ banking sector as the dependent variable and the co-exceedances in other states’ banking sector (along with other underlying variables of a banking system) as the explanatory variables.FindingsThe authors find smaller states transmit and receive more systemic shocks than their larger counterparts and larger states exhibit a better shock-resisting capacity than their smaller counterparts. The authors also find that bigger shocks are more contagious than the smaller shocks.Originality/valueThis will be the first paper that will investigate the inner linkage of US states’ banking network using three different distance to risk methods, thus providing timely guidance for regulators.

Systemic risk and macro-financial contagion in China: financial balance sheet-based network analysis

In this paper, first, using a dataset for China’s financial balance sheets we construct the sector-level macro-financial networks. Second, we analyse the statistical features and the dynamic changes of our macro-financial networks. Third, we conduct financial contagion simulations to identify the specific prominent sectors that can generate potential system-wide losses, and measure the resiliencies of different sectors to sector-specific financial shocks. The results uncover certain crucial propagation and contagion mechanisms of systemic risk in China’s macro-financial system. In addition, we find that the statistical features of the networks are closely associated with the losses incurred under the inter-sectoral financial contagion.

Systemic risk spillover across global and country stock markets during the COVID-19 pandemic

Uncovering the tail risk spillover among global financial markets helps provide a more comprehensive understanding of the information transmission in extreme market conditions such as the COVID-19 outbreak. In this paper, we examine systemic distress risk spillover between the global stock market and individual stock markets in the countries most affected by the COVID-19 pandemic. Using two important measures of tail dependence risk: conditional value at risk (CoVaR) and delta conditional VaR (ΔCoVaR), we apply the bivariate dynamic conditional correlation (DCC) conditional autoregressive heteroscedastic (GARCH) model. The empirical results reveal that bivariate systemic risk contagion between the global stock market and each individual stock market evolved during the sample period and intensified as COVID-19 spread worldwide. During the COVID-19 period, the developed markets in Europe and North America transmitted and received more marginal extreme risk to and from the entire global market than Asian stock markets. Further analysis involving the connectedness among the value at risk (VaR) series of the sampled stock market indices and the global stock index, shows a high degree of integration in the extreme downside risk of the stock market system, especially during the COVID-19 period. These findings offer practical implications for regulators, policymakers, and portfolio risk managers during the unprecedented uncertainty period provoked by the COVID-19 pandemic.

Systemic risk and financial contagion across top global energy companies

This study seeks to explore the firm-level interconnections in the fast changing and integrating global energy market. We investigate the risk connectedness using a Value-at-Risk (VaR) measure within a network composed of the top 20 global energy companies recognized by the Platts global energy company rankings, using the Diebold and Yilmaz's (2014) approach. We manage to identify the top risk contributors to the system across companies, regions and industries, and construct a total systemic risk index (TSRI) of the energy system. We further investigate which risk factors play a role in driving the evolution of the TSRI, again from a network perspective. The results show that its dynamics are mainly driven by the US stock market volatility and investors' sentiment in the financial market over the full sample, while energy market risks and exchange rate movements exert significant but short-term influences.

Systemic Risk in Chinese Commodity Futures Markets: A Graph Theory Analysis

This paper sets out to explore the contagion of systemic risk in Chinese commodity futures market based on specific tools of the graph-theory. More precisely, we use minimum spanning trees as a way to identify the most probable path for the transmission of prices shocks. In the sample of 30 kinds of Chinese commodity futures, we construct the MST and obtain the most probable and the shortest path for the transmission of a prices shock. We find that metal futures play an important role in commodity futures market and copper stands at the heart of the system (The core position of the system is very important for the transmission of system risk). And our results also reveal that when the risk occurs, the MST structure becomes smaller, leading to the most effective transmission path of risk becomes shorter.

How Does Exogenous Shock Change the Structure of Interbank Network?: Evidence from China under COVID-19

This paper utilizes China’s interbank deposit system under COVID-19 to examine how interbank network changes with considerable uncertainty surrounding exogenous shock. We investigate the interbank network in China by specifying the core-periphery pattern to disentangle the network development and the pandemic onset. In order to model the interbank deposit system, maximum entropy and partition-based approach are employed to estimate the bilateral exposures for banks and identify the network structure, respectively. We find that the four largest global systemically important banks (G-SIBs) in the world play a critical role in bearing interbank burden under this crucial period by increasing interbank assets and decreasing interbank liability. By comparing the situations before and after the outbreak of the pandemic in stress-testing, the empirical evidence shows that the number of core banks and capital of banks is declined in 2020Q2; however, the topological structure of the network is increasingly similar to a typical core-periphery pattern, and the changed interbank network can better absorb the loss and interrupt the contagion of systemic risk.

Systemic Risk Contagion in Reconstructed Financial Credit Network within Banking and Firm Sectors on DebtRank Based Model

This paper is dedicated to building a multilayer financial network within banking sectors and firm sectors (nonbanking) on the balance sheet of two types of agents and to assessing systemic risk contagion in the reconstructed network. Two propagation channels due to interbank credit and counterparty risk via banks’ loans to firms are comprehensively taken into account in systemic risk contagion assessment, which is based on the DebtRank model by analyzing the relative loss of each bank’s equity and the vulnerability of the network. The computational simulation on how systemic risk contagious process evolves has been conducted, where the possible influential factors of network structure, agent’s initial risk status, external shock ratio, liquidity flow rate, and different layers of the network are considered. The findings show that the reconstructed network is absolutely vulnerable under the assumed market circumstance without any bailouts and the risk contagion process shows nonlinear behavior. Specifically, when the average degree of the network and the external shock ratio increases, the risk contagion speed becomes relatively high and the resulting negative effects on the network are more intense. Besides, risks originating from the failed firms in bank-firm layer should place more negative effect on the financial system than that only happening in interbank market. Different liquidity rates in financial market could lead to obvious discrepancy of the risk contagion speed and the extent of asset loss. Additionally, the two layers of the network have diverse influences on risk contagious process resulting in totally different banks’ status in each layer.

Hisse Senedi Piyasalarında Frekans Bağlantılılığı ve Ağ Analizi: G-7 Ülkeleri Üzerine Bir Uygulama

In this study, we explore the cross-market volatility transmissions between equity markets in G-7 countries by employing the frequency connectedness method. By implementing this approach, we estimate the dynamic interaction mechanism of systemic risk among strongly interconnected financial markets during financial calm and distress periods. Additionally, we exhibit network topologies of directional spillovers to capture the financial connectedness of G-7 countries. The findings of the study propose that systemic risk contagion between G7 countries intensifies during financial turmoils and underlines the importance of an effective regulatory framework to monitor financial stress.

Systemic risk in the interbank market with overlapping portfolios and cross-ownership of the subordinated debts

In the banking network system, systemic risk affected by many factors. The cross-ownership of the subordinated debts is regarded as the accelerator of the spread of the systemic risk. In view of the systemic risk of banking network systems, considered interbank lending, overlapping portfolios, and cross-ownership of the subordinated debts, a multi-channel systemic risk contagion network model is proposed. In addition, in order to reflect the operation process of the banking network system more truly, the model introduces investment risk and allows banks to compensate for liquidity by depreciating assets. Based on the proposed model, the effects of deposit, investment, cross-ownership of the subordinated debts, and deposit reserve rate on risk contagion are studied and analyzed quantitatively. The proposed model indicates that cross-ownership of the subordinated debts is helpful to the stability of the banking network system when the banking network system suffered a small shock, but once the banking network system suffered a big shock, cross-ownership of the subordinated debts becomes the booster of risk transmission in the banking network system, which is liable to lead to the Domino effect of bankruptcy. This study provides a scheme for quantitative research on the systemic risk of the banking network system, which can be calibrated to real data. It also provides a reference for policy makers and regulatory authorities to guard against systemic risk of banking network systems.

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.

Prediction of Systemic Risk Contagion Based on a Dynamic Complex Network Model Using Machine Learning Algorithm

It is well known that the interbank market is able to effectively provide financial liquidity for the entire banking system and maintain the stability of the financial market. In this paper, we develop an innovative complex network approach to simulate an interbank network with systemic risk contagion that takes into account the balance sheet of each bank, from which we can identify if the financial institutions have sufficient capital reserves to prevent risk contagion. Cascading defaults are also generated in the simulation according to different crisis-triggering (targeted defaults) methods. We also use machine learning techniques to identify the synthetic features of the network. Our analysis shows that the topological factors and market factors in the interbank network have significant impacts on the risk spreading. Overall, this paper provides a scientific method for policy-makers to select the optimal management policy for handling systemic risk.

Banking network structure and transnational systemic risk contagion—The case of the European Union

This paper combines systemic contingent claims analysis with the conditional value-at-risk to construct a financial risk network, thereby providing a new method to measure the cross-risk of the banking sector among different countries. We apply this to a sample of listed banks in the European Union over the period 2007–2018. And we find that systemic risk is caused by the inherent and cross- risk. Cross- risk among countries is the dominant factor affecting systemic risk during the crisis period. In addition, the influence mechanism of risk network on the cross-risk exhibits asymmetry and displays the characteristic of “robust-yet-fragile”.

A Tail Dependence-Based MST and Their Topological Indicators in Modeling Systemic Risk in the European Insurance Sector

In the present work, we analyze the dynamics of indirect connections between insurance companies that result from market price channels. In our analysis, we assume that the stock quotations of insurance companies reflect market sentiments, which constitute a very important systemic risk factor. Interlinkages between insurers and their dynamics have a direct impact on systemic risk contagion in the insurance sector. Herein, we propose a new hybrid approach to the analysis of interlinkages dynamics based on combining the copula-DCC-GARCH model and minimum spanning trees (MST). Using the copula-DCC-GARCH model, we determine the tail dependence coefficients. Then, for each analyzed period we construct MST based on these coefficients. The dynamics are analyzed by means of the time series of selected topological indicators of the MSTs in the years 2005–2019. The contribution to systemic risk of each institution is determined by analyzing the deltaCoVaR time series using the copula-DCC-GARCH model. Our empirical results show the usefulness of the proposed approach to the analysis of systemic risk (SR) in the insurance sector. The times series obtained from the proposed hybrid approach reflect the phenomena occurring in the market. We check whether the analyzed MST topological indicators can be considered as systemic risk predictors.