Systemic Risk Contagion Research Articles

Robust-less-fragile: Tackling systemic risk and financial contagion in a macro agent-based model

We extend the Schumpeter meeting Keynes (K+S) agent-based model by introducing an evolving interbank network in the money market. Banks are exposed to counterparty risk and evaluate interbank positions using a network valuation (NEVA) clearing mechanism, which ensures systemic risk minimization with minimal assumptions on banks’ behavior. The model can replicate several stylized facts about the topology of the interbank network and the dynamics of banks’ balance sheets. The model encompasses financial contagion and systemic risk, allowing us to study the interactions between micro- and macro-prudential policies. Our results suggest that the introduction of a micro-prudential regulation also accounting for the network structure can reduce the incidence of systemic risk events. We also find that, in presence of a two-pillar regulatory framework – grounded on a Basel III macro-prudential regulation and a NEVA-based micro-prudential one –, there is no trade-off between financial stability and macroeconomic performance. This points towards the possibility of designing a regulatory framework able to achieve financial stability without overly stringent capital requirements.

From governance contagion to risk contagion: research on systemic risk contagion from the perspective of governance externality

From governance contagion to risk contagion: research on systemic risk contagion from the perspective of governance externality

Spillovers across global stock markets before and after the declaration of Russia’s invasion of Ukraine

Since the financial meltdown, studies on systemic risk and financial contagion have gained currency. Events like the COVID pandemic and the Russian invasion of Ukraine have fueled such an importance. This study examines the impact of the invasion on volatility transmissions across major stock markets worldwide. The stock indices considered in this study are ASX 200, ESTOXX 40, FTSE 100, HNGSNG, NIFTY 50, NIKKIE, and S&P 500. The work uses Vector Auto Regression (VAR) to study the transmission of returns. Later, the work performs Dynamic Conditional Covariance-Generalized Auto Regression Conditional Heteroskedasticity (DCC-GARCH) on the residuals where the transmission of returns was significant. The DCC-GARCH (E-GARCH) shows that all the asymmetric transmissions are negative. The study finds that co-movements of stock returns for the following pairs: ESTOXX 50-S&P 500, NIFTY 50-FTSE100, NIFTY 50-NIKKIE, NIKKIE-ESTOXX 50, S&P 500-NIFTY 50, and SP500-HNGSNG significantly intensified after the declaration of invasion. Such intensification of co-movements does establish the contagion effect triggered by invasion. The study shows that ESTOXX 50, which has the closest geographical proximity to the war zone, happens to be the highest generator of spillovers.

Ripple-Spreading Network of China’s Systemic Financial Risk Contagion: New Evidence from the Regime-Switching Model

Ripple-Spreading Network of China’s Systemic Financial Risk Contagion: New Evidence from the Regime-Switching Model

Financial risk contagion based on dynamic multi-layer network between banks and firms

Banks and firms play pivotal roles within the financial system and serve as primary channels for systemic risk contagion. While progress has been made in understanding risk contagion within financial network systems, many studies tend to overlook the intricate interplay of multi-channel risk transmission and the dynamic evolution of network structures. Leveraging complex network theory, we have developed a dynamic multi-layer financial network risk transmission model that encompasses traditional bank–firm lending relationships and incorporates dynamic short-term loans. Through our analysis of various parameters such as loan recovery ratios, cross-shareholding ratios, network structures, short-term loan ratios, and attack strategies, we have made significant observations: In scenarios where both the interbank lending network and cross-shareholding network among firms exhibit random network structure, the financial network presents the highest level of risk. Excessive deviations in network average degrees, either too high or too low, can also heighten network vulnerability. An increase in the recovery ratio of interbank loans and the cross-shareholding ratio among firms corresponds to an escalation in systemic risk. Nodes with higher lending assets, more liquid assets, greater total assets, or higher out-degrees within the network become significant contributors to an elevated network bankruptcy rate when subjected to targeted attacks. Notably, the dynamic short-term loan network significantly amplifies systemic risks, with this amplification effect intensifying as the scale of short-term loans increases. From both macro and micro perspectives, our findings furnish regulatory authorities with novel analytical tools and insights to effectively mitigate and manage systemic risks in the financial sector.

Oil prices and systemic financial risk: A complex network analysis

The risk associated with the dramatic volatility of oil prices has become an important factor affecting the stability of the financial system. Through a complex network viewpoint, this study uses firm-level data to investigate systemic financial risk contagion from the oil market to the financial markets. Our study considers the risk amplification effect in financial network and provides an in-depth empirical analysis of risk contagion among financial institutions under the oil market risk shock. According to the findings of this paper, systemic financial risk increases dramatically during oil crisis events. This paper also captures important characteristics of the network, including major risk receivers and risk transmitters, and finds that banks suffer more risk losses from the oil market. Furthermore, considering the important role of the financial system in the macro economy, the systemic financial risk caused by oil market risk has long-term negative effects on economic output.

Analyzing Systemic Risk and Financial Contagion: Interbank and Sentiment Networks

Analyzing Systemic Risk and Financial Contagion: Interbank and Sentiment Networks

The determinants of systemic risk contagion

The elevated interconnectedness of the global financial system has resulted in an increased frequency of financial crises, characterized by the swift transmission of turmoil between countries. This study introduces a novel quantile-connectedness-based contagion metric and investigates the drivers of systemic risk contagion, employing methodologies that address endogeneity and time-variation. We analyze data spanning two decades from 27 international banks and encompassing balance sheet-derived variables. Our findings indicate that contagion during the 2004–2021 period is largely driven by credit risk and leverage, while the impact of size and capital adequacy weakens after 2012. Furthermore, funding structure and profitability only display a significant effect during the 2014–2017 and Covid-19 periods, respectively. We also observe distinct peaks and troughs in each bank's systemic risk propagation, although they share commonalities with their counterparts. Given our findings, we suggest a holistic systemic risk surveillance model that employs high-frequency data and simultaneously incorporates multiple risk factors.

Systemic Risk Spillover Effects among China’s Financial Institutions: Evidence from the Spatial Econometric Model

This study investigates the spillover and contagion effects of systemic risk among Chinese financial institutions in terms of the conditional Value-at-Risk method and spatial econometric techniques. We construct different representative spatial weight matrices to demonstrate various risk connective categories and contagion channels. The spatial autoregression model is built to reveal the different magnitudes of systemic risk contagion effects and extended as the spatial quantile regression model to measure the change in spillovers across quantiles. The results highlight that the spatial agglomeration pattern of institution-level systemic risk is highly concentrated within the same sector but highly disparate between the different sectors. The closeness of the asset price channel and the information channel would enhance the systemic risk spillover effects among institutions. The higher the single institution’s systemic risk level is, the stronger its spillovers among all of them within the same financial department, yet contrarily, the spillovers are larger at lower quantiles between different sectors due to the disparate spatial tendency of systemic risk. Besides, the spillover effects across quantiles indicate the higher tail contagion of systemic risk spatial spillovers, especially during 2015 A-share market crash and 2020 COVID-19 outbreak.

Measurement and contagion modelling of systemic risk in China's financial sectors: Evidence for functional data analysis and complex network

We use the daily data of 45 listed financial institutions between January 1, 2008 and January 31, 2022 to conduct a functional data analysis (FDA) to measure the systemic risk of China's financial system and construct a complex network to analyze the contagion mechanism of systemic risk within and between sectors. We find that the constructed systemic risk indexes identify the influential events in the sample period and that the securities sector has a higher systemic risk than banking and insurance sectors do. The systemic risk contagion complex networks provide evidence of significant causal relationships between different sectors. Banking sector contributes a higher systemic risk and acts as a systemic risk transmitter in normal times, whereas securities and insurance sectors are more vulnerable during extreme events and may become sources of systemic risk during such events.

How Connected Is China’s Systemic Financial Risk Contagion Network?—A Dynamic Network Perspective Analysis

Modeling the effects and paths of systemic financial risk contagion is significant for financial stability. This paper focuses on China’s systemic financial risk from the perspective of dynamic networks. First, we construct a high-dimensional dynamic financial network model to capture risk contagion effects. Second, considering the ripple effect of financial risk contagion, we introduce and improve the basic model of the ripple-spreading network. Finally, small- and medium-sized banks and economic policy uncertainty are selected as the internal and external contagion source, respectively, to simulate the risk of ripple-spreading paths. The results show that financial contagion is more likely to occur within the same industry. The contagion triggered by internal shock first spreads within the same industry, and then to other industries. The contagion triggered by external shock first spreads to banks, then to diversified financial institutions, securities and insurance institutions, successively. Moreover, some small- and medium-sized commercial banks show strong abilities to spread risk ripples. The securities industry is the intermediary layer of the ripple network and plays a leading role in the ripple-spreading process. Therefore, systemic financial risk regulation should focus not only on large financial institutions but also on financial institutions with strong ripple effects. During major risk events, isolating risk intermediary nodes can cut off the paths of risk contagion and mitigate the impact on the whole financial system effectively.

Exploration of industrial risk contagion characteristics and mechanism under geopolitical events: evidence from China

ABSTRACT Based on the TVP-VAR-DY and TVP-VAR-BK models, this article examines the characteristics and mechanisms of systemic risk contagion in the Chinese industries under geopolitical events by selecting data spans from 1 January 2010 to 31 August 2022. First, dynamic analysis of full-sample risk contagion shows that there is a significant climb in total risk during geopolitical events. Then the static analysis of risk contagion in the full sample specifically shows the correlation between risk contagion and industry chain between the financial and real sectors. Besides, the sub-sample analysis illustrates that during geopolitical events such as the Sino-US Trade War, the COVID-19 Pandemic and the Russia-Ukraine Conflict, Chinese industrial stock indexes show short-term risk spillovers from key industries related to geopolitical events, and gradually spread along the industrial chain in the long run compared to the Chinese ‘Stock Market Crash’. Through further mechanistic tests, we find that the irrational behaviour of investors in the market exacerbates short-term risk contagion, while the financial distress of real firms due to financing constraints exacerbates long-term risk contagion. In addition, geopolitical risk, economic uncertainty, and policy uncertainty as macro variables also have an impact on the short-run and long-run risk contagion.

Systemic risk prevention policies targeting systemically important banks: Does clustering pattern matter?

It is well known that strengthening the soundness of individual banks that are too large or too interconnected is essential for maintaining financial stability. The clustering among homogeneous banks may also cause financial fragility but has received less attention. This paper discusses the policy improvement for preventing systemic risk from the perspective of the clustering pattern of systemically important banks (SIBs) based on a network optimization model. The results show that the clustering pattern of SIBs is closely related to systemic risk contagion. Remarkably, networks with fewer connections among SIBs exhibit lower systemic risk than those featuring apparent clustering of SIBs. The potential mechanism is that the systemic vulnerability of small and medium-sized banks is greatly reduced in the disassortative networks. The proposed tools based on this-inter-SIBs exposure limits and pairwise capital requirements-can guide network optimization and significantly reduce systemic risk. Furthermore, combining existing capital surcharges for SIBs (focusing on the stability of individual SIBs) and proposed network-based tools (focusing on the cluster structure of the network) will be an effective way to enhance financial stability over existing policies.

Systemic risk contagion of green and Islamic markets with conventional markets.

Financial markets are exposed to extreme uncertain circumstances escalating their tail risk. Sustainable, religious, and conventional markets represent three different markets with various characteristics. Motivated with this, the current study measures the tail connectedness between sustainable, religious, and conventional investments by employing a neural network quantile regression approach from December 1, 2008 to May 10, 2021. The neural network recognized religious and conventional investments with maximum exposure to tail risk following the crisis periods reflecting strong diversification benefits of sustainable assets. The Systematic Network Risk Index spots Global Financial Crisis, European Debt Crisis, and COVID-19 pandemic as intensive events yielding high tail risk. The Systematic Fragility Index ranks the stock market in the pre-COVID period and Islamic stocks during the COVID sample as the most susceptible markets. Conversely, the Systematic Hazard Index nominates Islamic stocks as the chief risk contributor in the system. Given these, we portray various implications for policymakers, regulatory bodies, investors, financial market participants, and portfolio managers to diversify their risk using sustainable/green investments.

Systemic Risk with Multi-Channel Risk Contagion in the Interbank Market

The systematicness of banks is an important driver of financial crisis. Overlapping portfolios and assets correlation of banks’ investment are important reasons for systemic risk contagion. The existing systemic risk models are all analyzed from one aspect and cannot reflect the real situation of the banking system. In the present paper, considering the overlapping portfolios and assets correlation, a contagion network model with multi-channel risk is proposed, which is with interbank lending (direct contagion channel), overlapping portfolios (indirect contagion channel), and assets correlation (indirect contagion channel). In addition, the model takes investment risk as an impact factor and learns the operation rules of the banking system to help banks compensate for liquidity through asset depreciation. Based on the proposed model, the effects of assets correlation, assets diversity, assets investment strategy, interbank network structure, and the impact of market density on risk contagion are studied and analyzed quantitatively. The method in this paper can more truly reflect the banking system risk than the existing model. This paper provides a solution for quantitative analysis of systemic risk, which provides powerful tools for macroprudential stress testing and a reference for regulatory authorities to prevent systemic risk.

Insurance institutional shareholding and banking systemic risk contagion: an empirical study based on a least absolute shrinkage and selection operator–vector autoregression high-dimensional network

Insurance institutional shareholding and banking systemic risk contagion: an empirical study based on a least absolute shrinkage and selection operator–vector autoregression high-dimensional network

Immunization of systemic risk in trade–investment networks

By constructing trade–investment networks and modeling the contagion of systemic risk in trade and investment channels within different countries, we design three immune strategies, namely, Random Immune, Target Immune, and Neighbor Immune, and compare the inhibitory effect of these strategies on the shock contagion of multilayer networks. Using bilateral trade and portfolio investment data between China and OECD countries from 2003 to 2019, results show that first, independent of the source of contagion, the network system with a self-healing capability always reaches the steady state after a certain period of the risk transmission process. The investment network is more resilient to systemic risk than the trade network. Second, Random Immune is inferior in intervening on important countries and currently provides weaker protection for trade–investment​ networks than two other types of immunization strategies because of the high concentration of trade and investment events in the networks. Third, Target Immune aiming at controlling the most important country, the US, is not optimized for improving risk resistance of countries cooperating scarcely with the US, but its optimization efficiency continues to increase as the significance of the US in the system keeps rising. Fourth, among the four Neighbor Immune approaches, Asset-Neighbor Immune provides the superior and consistently enhanced protection for the trade–investment networks, and Liability-Neighbor Immune has the dominant position in terms of the enhancement for risk resistance of each country’s trade and investment channels. Investment Asset-Neighbor Immune or Investment Liability-Neighbor Immune, which effectively reduces the negative impact of systemic risk on the whole system and all countries, is proposed to be implemented in trade–investment networks.

Modeling the Paths of China’s Systemic Financial Risk Contagion: A Ripple Network Perspective Analysis

Modeling the Paths of China’s Systemic Financial Risk Contagion: A Ripple Network Perspective Analysis

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

ABSTRACT 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 cores and capital of them 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.

Network Effects and Characteristics of Cross-Industrial Tail Risk Spillover in China

In recent years, the Chinese capital market has suffered several violent shocks, and the characteristics of systemic risk contagion across industries and markets have become increasingly important. It brings great potential danger to the stability of financial markets. Therefore, exploring the risk spillover among the real sectors has gradually attracted the attention of scholars. This paper examines the cross-industrial tail risk spillover network in the Chinese financial market. The characteristics and the dynamic contribution of each industry in the tail risk transmission chains are explored. We use the ∆CoES-ENGDFM-LVDN model based on monthly data from 2006 to 2020 to measure the tail risk of 28 industries in China and form a cross-industrial tail risk spillover network. The results show that different industries have different levels of spillover and importance in the network. Tail risk mainly spills over from the nonfinancial sector to the financial sector. The nonbank financial industry is the main recipient of tail risk spillover and is becoming progressively more important in the risk network. In addition, with the promotion of industrial structure, emerging industries such as communications, computers, and health care have begun to play more important roles in the tail risk spillover network in China. This paper not only enriches the research in the areas of tail risk spillover and systemic risk, but also has implications for regulators to maintain financial stability and prevent financial risks.