Contribution To Systemic Risk Research Articles

Investment network and stock’s systemic risk contribution: Evidence from China

In this paper, we investigated the effect of network structures on stock’s systemic risk contribution, which measures the connection characteristics of investment network from different aspects. Firstly, we find that network centrality increases systemic risk contribution, and empirical results hold even after controlling for other factors and are also robust to alternate measures. Secondly, this paper further proposes two possible explanations. Specifically, investment network connection could increase the possibility of collusion with firms, facilitate the relevant institutional investors to hollow out the listed company, and firms at the center of network can amplify the sentiment of market participants through the generation and dissemination of information, thereby increasing stock’s systemic risk contribution. Besides, economic policy uncertainty (EPU) could strengthen the positive effect of network centrality on stock’s systemic risk contribution. Finally, we document that other important network features (including structural holes, clustering coefficients, and core-periphery structure) can also increase stock’s systemic risk contribution.

Foreign Capital in Polish Banks – Its Contribution to Systemic Risk and Level of Concentration

Theoretical background: An important source of systemic risk in the banking system is the ownership structure, especially the ratio of foreign capital, which affects not only the results of individual banks but also the situation of the entire sector during the financial crisis. The effects of foreign capital engagement in a local banking system not always have to be exclusively positive. In particular, this refers to the systemic risk contribution of foreign capital in the national banking system as well as foreign capital concentration and potential systemic risk transfer through foreign capital. These threats are becoming especially important for the Polish banking sector, where foreign capital to assets ratio – despite its gradual decrease since 2008 – is still higher than 40%. Purpose of the article: The aim of this article is to assess foreign capital systemic risk contribution in the Polish banking system and the scale of threat posed by possible transfer of this risk through foreign capital. Research methods: Two methods were employed in this study, i.e. (1) supervisory measure of individual banks’ systemic risk contribution, including the guidelines of the European Banking Authority, and (2) SRISK% measure, which shows which part of the systemic capital shortfall is generated by which bank. The systemic capital shortfall, on the other hand, is defined as a sum of additional own funds necessary to meet the regulatory minimum should a 40% fall of total capitalisation of the banking system occur within the subsequent 6 months. At the same time, owing to the application of both of the above methods, we could draw on their strengths while eliminating the identified weaknesses. Main findings: Our analyses did not indicate that the level of foreign capital concentration in the Polish systemically important banks may contribute to systemic risk transfer through foreign capital. Although all Polish banks owned by foreign capital, identified as systemically important, are subsidiaries of the banks which – in their home countries – are also considered as systemically important, their contribution to the Polish systemic risk is generally a few times lower than systemic risk contribution of their parent institutions, with the exception of one bank, which should be carefully monitored by the Polish financial safety net. The rising systemic risk contribution of the banks owned by foreign capital and identified as systemically important in the Polish banking system should not be a matter of concern thanks to the diversified share of individual banks in it. The conclusions drawn in the study are important both in terms of the policies adopted by the safety net institutions and from the point of view of further improvement of systemic risk measures, especially those assessing the contribution of individual banks to this risk.

Measuring systemic risk contribution: A higher-order moment augmented approach

Marginal contributions of individual institutions to the systemic risk contain predictive power for potential future exposures and provide early warning signals to regulators and the public. In this paper, the higher-order co-skewness and co-kurtosis are used to construct systemic risk contribution measures, which allow us to identify and characterize the co-movement driving the asymmetry and tail behavior of the joint distribution of asset returns. We illustrate the usefulness of higher-order moment augmented approach by using 4868 stocks living in the Chinese market from June 2002 to March 2022. The empirical results show that these higher-order moment measures convey useful information for systemic risk contribution measurement and portfolio selection, complementary to the information extracted from a standard principal components analysis.

Are stress‐tested banks in the United States becoming similar? Evidence from convergence tests

AbstractUS bank stress tests were introduced to improve the risk posture and management practices of large and complex banking institutions. We investigate whether stress‐tested banks in the United States converge to each other in their levels and determinants of profitability, as well as their risk taking and systemic risk contributions. Our results are consistent with convergence in profitability, asset quality, management quality, securitization income, and income diversification of stress‐tested banks. Our difference‐in‐differences estimation results provide causal evidence of stress tests leading to convergence in income diversification. Furthermore, stress‐tested banks converge in their income diversification strategies, return volatility, default risk, leverage risk, and systemic risk contributions. This study sheds light on a situation where these banks may simultaneously have exposure to the same risks, leaving the financial system more vulnerable to a crisis as a result of the exposed risk.

Bank liquidity hoarding and bank systemic risk: The moderating effect of economic policy uncertainty

In this article, we use panel data from 457 listed banks in 20 countries to investigate the impact of bank liquidity hoarding on systemic risk in the banking sector and discuss it under the condition of economic policy uncertainty (EPU). Empirical evidence suggests that when banks' liquidity hoarding increases, their systemic risk contribution decreases significantly. Furthermore, we demonstrate that bank liquidity hoarding works through the channel of improving bank operational stability and reducing bank asset risk. In addition, during a period of high EPU, bank liquidity hoarding has a more significant effect on reducing systemic risk. Our results remain stable after a battery of robustness checks. Heterogeneity analysis indicates that the role of bank liquidity hoarding in reducing systemic risk is more significant in larger banks and banks in developed countries. Moreover, this effect is particularly pronounced when there is an accommodative monetary policy environment. Our study has some important implications for policymakers and regulators. Previous research suggests that policymakers may consider encouraging the banking sector to actively create liquidity instead of hoarding it, as this could lead to higher economic output. However, from the perspective of financial stability, banks' liquidity hoarding can help reduce their systemic risk contribution, especially in periods of high external uncertainty. Therefore, policymakers need to evaluate bank liquidity hoarding behaviour more comprehensively, and strike a balance between promoting economic growth and ensuring financial system stability to maximize overall welfare.

Building resilience in cybersecurity: An artificial lab approach

AbstractBased on classical contagion models we introduce an artificial cyber lab: the digital twin of a complex cyber system in which possible cyber resilience measures may be implemented and tested. Using the lab, in numerical case studies, we identify two classes of measures to control systemic cyber risks: security‐ and topology‐based interventions. We discuss the implications of our findings on selected real‐world cybersecurity measures currently applied in the insurance and regulation practice or under discussion for future cyber risk control. To this end, we provide a brief overview of the current cybersecurity regulation and emphasize the role of insurance companies as private regulators. Moreover, from an insurance point of view, we provide first attempts to design systemic cyber risk obligations and to measure the systemic risk contribution of individual policyholders.

The Importance of Non-Systemically Important Banks—A Network-Based Analysis for China’s Banking System

There is important theoretical and practical significance to scientifically identifying the systemic importance of banks for effectively preventing and controlling systemic risks in the banking system. Prevalent identification methods are biased because they only pay attention to measuring the systemic risk contribution of individual banks to the whole system in order to determine that bank’s systemic importance. Less attention is paid to the cascade effects of risk spillover among banks. This study proposes a novel method for measuring the cascade effects of risk spillover of banks and their contributions to systemic risks by building up a conditional tail risk network of China’s banking system. Different from previous analyses of systemic risks based on the identification and risk measurement of systemically important banks (SIBs), this paper focuses on analyzing the risk spillover effects of non-SIBs and their contributions to systemic risks by building up a conditional tail risk network of China’s banking system. Our empirical results show that some non-SIBs in China are more vulnerable to the shocks of systemic risk than SIBs, and that they are more likely to act as key intermediaries to transmit risk to SIBs, thereby triggering systemic risk. In view of this, we propose to identify key non-SIBs according to their risk spillover intensity because they are also systemically important. The market regulators not only need to pay attention to SIBs that are too big to fail, but also treat seriously the key intermediaries of “risk spillover too strong to fail” in the network in order to ensure the stability of the banking system.

Macro-prudential policy and systemic risk of real estate firms: Evidence from China

Using data of listed real estate firms (REFs) in China, we examine how macro-prudential policy affects their systemic risk. Employing a two-dimensional measure of systemic risk contribution and vulnerability, we find that macro-prudential policy reduces both dimensions of systemic risk. Deleveraging and lowering risk interconnectedness are two plausible channels. Asset-based policy instruments, including loan-to-value limits, are more effective than capital- and liquidity-based instruments. Moreover, rational use of macro-prudential tools helps stablize REFs’ operating performance by reducing their systemic risk. Our findings highlight the critical role of macro-prudential policy in balancing risk prevention and economic growth.

Systemic risk in European banks: Does ownership structure matter?

We empirically test whether ownership concentration explains the cross-variation in systemic risk contribution for a sample of European banks over the 2004–2021 period and how this effect may vary depending on the category of the largest controlling shareholder. We explore two potential contagion channels: the risk-taking incentives and banks’ assets commonality. The results show that higher ownership concentration is associated with greater banks’ systemic risk contribution. Moreover, we find that banks’ systemic risk contribution is even stronger for banks where institutional investors or States are the largest controlling owners. Overall, our findings contribute to the literature examining the determinants of banks’ systemic risk in particular and financial stability as a whole and have several policy implications.

The risk spillover of high carbon enterprises in China: Evidence from the stock market

This paper examines the time-varying risk spillover among high carbon enterprises by constructing Granger-causality networks from 2011 to 2021, and explore the types of high carbon enterprises that have stronger risk spillover capabilities. Additionally, we also expanded our analysis on the risk spillovers from high carbon enterprises to non-high carbon enterprises. We find that the intensity of risk spillover among high carbon stocks strengthened from 2013 to 2016, then weakened, and slightly re-strengthened in 2020. High carbon enterprises from power, steel, aviation, and petrochemical industries occupy central positions in the networks. Moreover, fundamental factors such as asset scale, profitability, capital structure, ownership, and industry category significantly influence the connectivity and centrality of high carbon enterprises in the networks. Meanwhile, the high carbon enterprises with higher connectivity and central location in the networks have greater systemic risk contributions. Finally, we find that the non-high carbon enterprises from agriculture, forestry, animal husbandry and fishery industry, mining industry and transportation, warehousing and postal industry are more susceptible to the risk overflow from high carbon enterprises.

Measuring systemic risk with high-frequency data: A realized GARCH approach

This paper incorporates high-frequency information to measure systemic risk. Under the Multivariate Realized GARCH framework, we compute the CoVaR measure using a multivariate skew-t distribution. Using 5-minute data of 98 U.S. financial institutions from 2000 to 2022, we show the empirical improvement of the high-frequency measurement. We also investigate the relationship between institutions’ systemic risk contributions and firm-level characteristics. Our empirical findings suggest that firm size and leverage are positively related to institutions’ contributions to systemic risk.

Systemic risk in non financial companies: Does governance matter?

The paper investigates the impact of four key corporate governance mechanisms - board, audit, compensation and ownership, and anti-takeover provisions - on the exposure and contribution to systemic risk of >400 US non-financial companies (NFCs) listed in S&P500 from 2005 to 2020. Our results show that in NFCs, unlike in banks, good corporate governance practices constrain both systemic risk exposure and contribution. We find a complementary effect between internal corporate governance mechanisms in reducing both the contribution and the exposure to systemic risk, and a substitution effect between internal and external governance practices in constraining the exposure of NFCs to systemic risk. Moreover, strong corporate governance practices are shown to constrain systemic risk both in steady-state conditions and in times of distress.

Identification of systemically important banks in China based on Merton-Shapley model and a comparison between different indicators

Accurately measuring systemic risk in the banking industry and identifying systemically important banks are significant parts of macro-prudential supervision. However, there is a lack of research on measuring systemic risk and identifying important banks under the same framework. And there are relatively few studies comparing different indicators of systemically important banks in the context of China's banking industry. Therefore, this paper first constructs a new Merton model to measure the dynamically evolving systemic risk of China's banking system. Then combine the Merton model with the Shapley value to construct the Merton-Shapley framework, and propose a new indicator MShv to identify systemically important banks. At last, use the data of China's banking industry from 2006 to 2019 to conduct an empirical study. The research results show that the systemic risk in China peaked in 2008 and 2015 near the stock market crash, after 2008 the risk appeared a fluctuating decline. And it is found that MShv can accurately identify important banks and simultaneously is conducive to policy implementation. By a comparison between MShv and other systemic risk contribution indicators such as ΔCoVaR, MES, and SRISK, we find that there are many differences for different indicators in the applicability of identifying systemically important banks, the information reflected, and so on. Regulators can consider using several indicators synthetically to supervise banks during different risk periods, in order to maintain the stability of the financial system.

Modifying (M)CoVaR and constructing tail risk networks through analytic higher-order moments: Evidence from the global forex markets.

In a financial system, entities (e.g., companies or markets) face systemic risk that could lead to financial instability. To prevent this impact, we require quantitative systemic risk management we can carry out using conditional value-at-risk (CoVaR) and a network model. The former measures any targeted entity's tail risk conditional on another entity being financially distressed; the latter represents the financial system through a set of nodes and a set of edges. In this study, we modify CoVaR along with its multivariate extension (MCoVaR) considering the joint conditioning events of multiple entities. We accomplish this by first employing a multivariate Johnson's SU risk model to capture the asymmetry and leptokurticity of the entities' asset returns. We then adopt the Cornish-Fisher expansion to account for the analytic higher-order conditional moments in modifying (M)CoVaR. In addition, we attempt to construct a conditional tail risk network. We identify its edges using a corresponding Delta (M)CoVaR reflecting the systemic risk contribution and further compute the strength and clustering coefficient of its nodes. When applying the financial system to global foreign exchange (forex) markets before and during COVID-19, we revealed that the resulting expanded (M)CoVaR forecast exhibited a better conditional coverage performance than its unexpanded version. Its superior performance appeared to be more evident over the COVID-19 period. Furthermore, our network analysis shows that advanced and emerging forex markets generally play roles as net transmitters and net receivers of systemic risk, respectively. The former (respectively, the latter) also possessed a high tendency to cluster with their neighbors in the network during (respectively, before) COVID-19. Overall, the interconnectedness and clustering tendency of the examined global forex markets substantially increased as the pandemic progressed.

Systemic Risk Contribution and Bank’s Competitiveness

There are two competing views about bank’s competitiveness and its systemic risk contribution: competition-stability and competition-fragility. Previous research shows mixed results. To test empirically the relationship, this research proposes a quadratic functional form that may reconciliate these two opposite views. Using Marginal Expected Shortfall as individual bank’s systemic risk contribution measurement and Lerner Index as individual bank’s competitiveness, this research find that the relationship resembles U-shape. In the first phase, competition creates prudent banking operation and low systemic risk contribution. But when competition become excessive, competition drive dominant bank to be a systematically important financial institution which may cause a serious systemic defaults and threat financial system stability.

Bank contribution to financial sector systemic risk and expected returns: Evidence from large U.S. banks

We estimate the contribution of large U.S. banks to the financial sector systemic risk by using value-at-risk (VaR), conditional value-at-risk (CoVaR), and two-stage least square (2SLS) methodology. Our sample is the monthly stock returns of 25 large U.S. banks from 1997 to 2021. We find that banks contributing more to the systemic risk have lower future returns on average. We also sort the portfolios’ future returns into five pentiles based on systemic risk contribution (SRC) and find that portfolios with high SRC earn lower future returns than those with low SRC. Our second contribution to the literature is the indication of the endogeneity problem in the SRC measures. We suggest an identification strategy for the estimation of SRC measures. Our results are contrary to some of the earlier studies, which concluded that the Dodd–Frank act of 2010 failed to eliminate the too-big-to-fail problem in banks. Such studies showed that anticipation of government subsidies has not been eliminated in the form of higher expected returns even for banks contributing more to systemic risk of the financial system. The results in our present study open a new research direction and are useful for investors and policymakers.

How does CEO power and overconfidence affect the systemic risk of China’s financial institutions?

The purpose of this paper is two-fold. First, this study measures the contribution of banks and non-bank financial institutions toward the systemic risk of China. Second, the present study investigates the relationship between CEO power, CEO overconfidence, and systemic risk. This study uses the Delta Conditional Value-at-Risk (∆CoVaR) method to measure the systemic risk contribution of firms listed on the Shenzhen and Shanghai stock exchanges over a period of 2006–2018. The results show that non-bank financial institutions are systemically more important compared to banks. We employed fixed-effect regression analysis to show that banks with overconfident CEOs increase the firm’s systemic risk. The results also confirm that powerful CEOs enhance the contribution of non-bank financial institutions to systemic risk, whereas CEO power’s impact was significant only for non-state-owned banks. The findings were further validated by the robustness test results obtained using the two-stage least squares approach. These findings are important for constructing regulations to reduce the contribution of firms to systemic risk.

Bank herding and systemic risk

• We study banks' herding in portfolios and their systemic risk contributions. • Pose and test alternative hypotheses using novel measures of herding. • Nuanced empirical results differ by portfolio, bank size, and periods. • Evidence of "too-many-to-fail" effects on perceptions of herding and systemic risk. Bank herding behavior is often hypothesized to increase systemic risk, but the actual effect is unclear ex-ante from the theory and unknown ex-post from the data. We expand the literature on this topic in several dimensions – posing alternative hypotheses regarding the effects of herding in asset, liability, and off-balance sheet portfolios; developing a novel set of bank-specific, time-varying measures of herding in these portfolios; and empirically testing the relations between bank herding for all three portfolios and bank systemic risk contributions. We find nuanced empirical results that differ by portfolio, bank size class, and periods before versus after TARP.

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.

ESG and systemic risk

ABSTRACT How do changes in Environmental, Social and Governance (ESG) scores influence banks’ systemic risk contribution? Using a dynamic panel model, we document a beneficial impact of the ESG Combined Score and Governance pillar on banks’ contribution to system-wide distress analysing a panel of 367 publicly listed banks from 47 countries over the period 2007–2020. Stakeholder theory and theory relating social performance to expected returns in which enhanced investments in corporate social responsibility mitigate bank-specific risks explain our findings. However, only better corporate governance represents a tool in reducing bank interconnectedness and maintaining financial stability. The results are robust to alternative measures of systemic risk, both contribution and exposure, as well as when estimating a static model. Our findings stress the importance of integrating banks’ ESG disclosure into regulatory authorities’ supervisory mechanisms as qualitative information.