Systemically Important Banks Research Articles

Banking-as-a-service? American and European G-SIBs performance

PurposeThe purpose of this study is to investigate how the implementation of digital banking services (mobile applications) by globally systemically important banks (G-SIBs) affects banks’ performance in the USA and Europe from 2005 to 2022.Design/methodology/approachThe study employs advanced econometric methods to analyze the link between deposits and banking performance, utilizing linear regressions and multivariate Bayesian regressions.FindingsOur results indicate that customer deposits positively impact a bank’s performance after the introduction of the mobile application feature of check deposits, whereas social risk negatively impacts banking financial performance. These findings support the hypothesis that technology implementation improves the profitability and growth of traditional banks.Research limitations/implicationsWhile findings are robust econometrically in linear and Bayesian regressions, variables reflecting the digitalization of banks remain limited. For instance, the number of mobile users or the volume of digital transactions per bank since the implementation of the mobile app is not available.Practical implicationsIn a rapidly growing technology and constantly changing customers behaviors, this research has practical implications from bankers’ perspective to continue the technological innovation efforts and from regulators’ perspective to strengthen requirements for the digital banking services.Social implicationsWe provide empirical evidence that including a banking app for smartphones’ users for remote banking services benefit the financial performance of banks. However, the social risk remains significant for banks in terms of customers' satisfaction, data privacy and cybersecurity.Originality/valueThis paper employs an innovative approach to create a mobile app “discriminatory” factor and examine the relationship between deposits and banks’ performance before and after the introduction of a mobile app for too-big-to-fail banks in Europe and the USA. Additionally, we consider the social risk component of the ESG score, as a bank’s decision to implement mobile applications and technology for its customers potentially affects social risks associated with customer satisfaction and technology usability.

Is There Gap between Operating Performance of Systemically Important and Non-Systemically Important Banks in China?--Empirical Study based on Public Panel Data after Release of List

On September 22, 2023, the People’s Bank of China (PBOC) and the State Administration for Financial Regulation (SAFS) released the latest list of China Systemically Important Banks (D-SIBs). This study aims to analyze whether there is a gap in operational performance between (D-SIBs) and (D-SIBs) under additional regulation. The research method uses independent sample t-tests in statistics and the AHP_DEA model for financial bank operational performance. The research results indicate that there are differences in the operational performance of systemically important banks and non-systemically important banks in certain indicators. systemically important banks have a larger share in the entire banking system, and systemically important banks face more regulatory constraints than non-systemically important banks. This makes the cost of capital restructuring for systemically important banks higher, thereby reducing the speed of capital restructuring. However, further analysis indicates that there is no significant difference in operational performance and risk control between banks with systemic importance and nonsystemic influence. In view of this, systemically important banks must invest in technology and innovation to improve operational efficiency.

Study of prerequisites for the formation of systematically important banks in regional economic associations

Despite the fact that more than ten years have passed since the rules for domestic systemically important banks (D-SIBs) were developed, the issues of identification of systemically important banks (SIB) and their regulation remain relevant today. Of particular interest is the possibility of forming uniform criteria for identifying systemically important banks within the framework of regional economic associations. The study is aimed at studying the prerequisites and fundamental possibilities of creating common criteria for classifying banks of the participating countries (Kazakhstan, Belarus, Russia) as SIB at the level of the Eurasian Economic Union (EAEU) through a clusterization approach. Clustering methodologies using Euclidean distance metrics and the Hamming model allowed for an initial study on the potential of the participating countries to develop common criteria for the SIB of the EAEU Union. The results of the analysis showed that the participating countries have the potential and some prerequisites for the development of common criteria within the union. However, it was found that the systemically important banks of the participating countries often change the level, since the criteria are not stable. Therefore, when forming uniform criteria, this point should be taken into account and criteria should be developed that are not approximated with each other, and similar features are significantly distinctive. The study consists of two parts, this work is the first part.

Social media-based implosion of Silicon Valley Bank and its domino effect on bank stock indices: evidence from advanced machine and deep learning algorithms

Social media-driven speculations play a crucial role in triggering the collapse of the banking system and stock markets. In this paper, we investigate the effect of Twitter-based investor sentiment on the collapse of Silicon Valley Bank (SVB), the 16th largest bank in the US. Additionally, we examine the spillover effect of the social media-based investor sentiment and SVB collapse on the bank stock indices from twelve countries where Global Systemically Important Banks (G-SIBs) operate. Advanced machine and deep learning models are employed to model the social media-based investors’ sentiment regarding SVB implosion and its spillover effect on the G-SIBs’ bank stock indices. Our results reveal that social media-based negative investors’ sentiment played an important role in SVB implosion. Our results further show that the negative investors’ sentiment persisted, and its systemic shock was transmitted to the G-SIBs bank stock indices. Importantly, our results provide a lead and lag relationship between investors’ sentiment and returns of G-SIBs bank stock indices. The findings of this study offer crucial insights for policymakers to consider the external shocks associated with social media-based investors’ sentiment when devising policies related to bank runs, thus helping to prevent future financial crises and cross-border contagion.

Does the Too-Big-To-Fail status affect depositor’s discipline: a Gaussian mixture model algorithm approach

PurposeThis study empirically demonstrates a contradiction between pillar 3 of Basel norms III and the designation of Systemically Important Banks (SIBs), also known as Too Big to Fail (TBTF). The objective of this study is threefold, which has been approached in a phased manner. The first is to determine the systemic importance of the banks under study; second, to examine if market discipline exists at different levels of systemic importance of banks and lastly, to examine if the strength of market discipline varies at different levels of systemic importance.Design/methodology/approachThis study is based on all the public and private sector banks operating in the Indian banking sector. The Gaussian Mixture Model algorithm has been utilized to classify banks into distinct levels of systemic importance. Thereafter, market discipline has been observed by analyzing depositors' sentiments toward banks' risk (CAMEL indicators). The analysis has been performed by employing the system Generalized Method of Moments (GMM) to estimate models with different dependent variables.FindingsThe findings affirm the existence of market discipline across all levels of systemic importance. However, the strength of market discipline varies with the systemic importance of the banks, with weak market discipline being a negative externality of the SIBs designation.Originality/valueBy employing the Gaussian Mixture Model algorithm to develop a framework for categorizing banks on the basis of their systemic importance, this study is the first to go beyond the conventional method as outlined by the Reserve Bank of India (RBI).

Heterogeneous dependence of the FinTech Index with Global Systemically Important Banks (G-SIBs)

This paper aims to investigate the Granger causality relationship in quantile between the FinTech Index and globally systemically important banks (G-SIBs). The result was observed that at the median and under conditions of extreme quantiles in the FinTech Index, there was no Granger causality relationship between the FinTech Index and the vast majority of systemically important banks. Our research offered vital insights to regulatory agencies, highlighting the importance of monitoring market conditions at higher or lower quantiles to prevent the impact of financial technology on G-SIBs and to maintain global financial stability.

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

Interconnectedness between stock and credit markets: The role of European G-SIBs in a multilayer perspective

This paper investigates the interplay between two types of banking risk: market and credit. By verifying the volatility feedback loop hypothesis, we employ a multilayer information spillover network to explore information flow (risk spillover) between market and credit risks of European Global Systemically Important banks (G-SIBs). We analyse their role in transmitting market and credit risk, showing that capturing spillovers of both risks provides a more comprehensive perspective on financial risk contagion. Our findings have important implications for policymakers and risk managers, aiding in better risk assessment and timely crisis response, improving financial stability.

Ranking systemically important financial institutions of the Chinese financial system: evidence from the higher-order temporal network

ABSTRACT Identifying systemically important financial institutions (SIFIs) plays a key role in regulating systemic risk. We construct higher-order temporal causal networks based on daily stock return data and rank the systemic importance of 45 listed financial institutions. The constructed networks provide evidence that financial institutions become more interrelated during crisis periods, and capture the higher-order dynamic characteristics of interconnectedness among institutions. The ranking results show that not only the largest financial institutions, but also smaller, highly interconnected institutions are systemically important and need to be regulated. Our findings highlight the importance of considering higher-order interconnectedness among financial institutions when labelling SIFIs.

Volatility spillover features in financial industries and identification of systemically important financial institutions: A new perspective

We study the spillover effects between financial industries and the identification of systemically important financial institutions (SIFIs) from a new perspective. The total volatility is decomposed into two categories, i.e., good volatility triggered by positive returns and bad volatility triggered by negative returns, which are applied to the volatility spillover index (the DY spillover index) model (Diebold and Yilmaz, 2012; 2014) to study the volatility spillover effects between different financial industries based on different types of volatility. We construct the total, good, and bad volatility spillover networks and use PageRank to measure the systemic importance of financial institutions based on the three networks and compare them with the risks of financial institutions obtained by the three risk measurement methods of ΔCoVaR, MES, and SRISK. By decomposing total volatility into good volatility triggered by positive returns and bad volatility triggered by negative returns, the positive and negative impacts of different financial industries during different periods can be effectively identified to accurately evaluate the risk levels of different financial industries. The results indicate that the PageRank rankings of financial institutions calculated by the bad volatility spillover network have the highest correlation with the risk rankings of financial institutions calculated by ΔCoVaR, MES, and SRISK. The findings suggest the necessity of decomposing the spillover effects between financial industries into good spillover effects triggered by positive returns and bad spillover effects triggered by negative returns, providing a new way of identifying SIFIs.

The impact of risk spillover network-based multidimensional systemic risks on the real economy

ABSTRACT Multidimensional systemic risk measures are constructed based on dynamic risk spillover networks to quantify the level, transmission speed, and transmission breadth. To investigate the significance, direction, magnitude, and persistence of the impacts of systemic risks on future real economic distribution, we combine the quantile regression model and Bootstrap quantile t test method. Finally, the heterogeneity of the impacts of systemic risks and SIFIs (systemically important financial institutions) risks is analysed. Our empirical results find that 1) most bank companies take the role of risk drivers in the financial system, while most security, insurance, trust, and real estate companies take the role of risk distributors and risk receivers. 2) the total spillover index, average shortest path length, and average clustering coefficient have significant impacts on the left tail and central tendency of future real economy, and the latter two measures also have significant impacts on the right tail. 3) SIFIs risks have earlier and greater impacts than Non-SIFIs (non-systemically important financial institutions) risks. Multidimensional systemic risks contain additional information to identify financial risks earlier than single institutions risks. 4) the impacts of financial risks on the real economy are persistent. The superimposition of impacts may trigger extreme risks.

SUSTAINABILITY REPORTING: EVIDENCE FROM GLOBAL SYSTEMICALLY IMPORTANT BANKS (G-SIBs)

In recent years, the issue of environmental pollution and climate change has led the scientific debate to focus on social issues, environmental protection, and human rights, deepening the topic of sustainable economic development. To provide stakeholders evidence on economic, environmental, and social performance, all corporate organizations must supplement traditional financial reporting tools with Non-financial information, following numerous standards, frameworks, and guidelines. The topic of Non-financial disclosure is widely investigated with reference to industrial companies, while the financial institution sector represents a context that is still little explored. Indeed, financial institutions are significant promoters of Corporate Social Responsibility (CSR), and even if their operations do not have direct impacts on the environment, they can encourage the allocation of their capital in socially responsible investments, thus contributing to the spread of sustainability culture in the financial system and among citizens. This paper analyzes the main sustainability frameworks, standards, and guidelines used by global systemically important banks (G-SIBs) for disclosure on environmental aspects along with social and governance factors, investigating the attention degree of financial institutions toward the sustainability issue.

Smart systemic-risk scores

This paper proposes a new systemic-risk score to identify and regulate global systemically important banks (G-SIBs) by using an alternative weighting scheme based on volatility to aggregate all systemic-risk facets. Following a portfolio management approach, I equalize the risk contribution of each systemic-risk component to the cross-sectional volatility of the smart systemic-risk scores. The equally-weighted risk contribution (ERC) method appears to be a relevant alternative to the cap on the substitutability category. To discriminate between several systemic-risk scores, I modify and apply the axiomatic framework of Chen et al. (2013) to express supervisor preferences among systemic-risk scores. Such preferences are based on the expected value of the cross-sectional dispersion of systemic-risk scores over the years.

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.

Network effects on risk co-movements: A network quantile autoregression-based analysis

Interconnections and volatilities among financial institutions, notably during periods of extreme risk events, are fundamental factors in studies of financial markets. Utilizing tail-event-driven networks, we depict the risk interdependencies among financial institutions and introduce a novel ranking strategy for institutional influence based on network effects and quantile regression techniques. This interdependence is examined through the prism of joint extreme movements, utilizing bivariate extreme value theory and classification analysis. To validate their effectiveness and applicability in the real world, we deploy these methodologies on Systemically Important Financial Institutions (SIFIs). Additionally, we provide a ranking of the importance of eight U.S. institutions based on tail risks, offering regulators valuable insights and references.

TLAC bonds and bank risk-taking

This study explores the relation between total loss-absorbing capacity (TLAC) bonds and bank risk-taking. TLAC bonds contain a unique instrument in that they entail “structural subordination,” which enables them to be converted into equity to resolve a failure of subsidiaries of global systemically important banks (G-SIBs). Given the debate in the literature over the impact of subordinated bonds on issuers’ risk-taking, we find that a larger amount of TLAC bond issuance is associated with higher risk. Although TLAC regulation is designed to mitigate systemic risk, we unveil the opposite result, which may increase the probability of G-SIB default.

Stress testing bank insolvency risk by systemic equity market shock: An expected shortfall approach

This paper proposes a simple intuitive approach for assessing and stress testing the insolvency risk of global systemically important banks (G-SIBs) by shocking systemic risk in the equity market. In particular, the paper introduces two metrics to measure the relative adequacy of total loss absorbing capacity (TLAC) upon resolution of a bank under both real market conditions and stress test settings. The metrics may also be calculated on a regular basis for monitoring bank insolvency risk. Three global systemically important banks, including Credit Suisse, are presented as examples for the analysis. To capture the heavy tail feature of equity market downturns and ensure conservatism for stress testing, an analytical framework is adopted based on extreme value theory with expected shortfall, power law distributions and copula method. The analysis concludes that G-SIBs may have a high probability of loss exceeding TLAC under systemic market stress.

Can a pro-public orientation explain the holding of capital by G-SIBs?

Purpose: We investigate the correlation between capital structure and a set of, mostly, standard capital structure determinants for a unique sample: Global Systemically Important Banks (G-SIBs).Design/methodology/approach: We augment the standard set of regression determinants with a proxy measure of pro-public orientation (DataStream’s Refinitiv Environmental, social and governance [ESG] scores). We expect to find that a more pro-public orientated G-SIB holds more capital. This is because very large and systemic banks underpin the functioning of society. The public, therefore, has a direct interest in bank safety with a better capitalised bank being a safer bank. On the other hand, shareholders of a safer bank suffer because of lower profitability.Findings/results: Initial results indicated no relation between pro-public orientation and bank leverage; however, further analysis showed that bank leverage decreases as the governance component score increases. This suggests that the governance of G-SIBs is important for financial stability. Bank size was found to have no intermediation effect on the relationships, implying that our results are not because of a clustering among the largest banks. Correlations between the control variables and bank leverage provide support for the argument that bank leverage is not solely determined by regulations.Originality/value: We extend recent work on social ratings and capital structure in non-financial firms to banks. Our results provide further support for the proposition that the drivers of the capital structures of non-financial firms also determine those of banks, weakening the argument that capital regulation is the sole determinant of bank capital structures. Our sample focuses attention on a core financial decision of very important, if not the most important, players in the global economy.

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.

A new Copula-CoVaR approach incorporating the PSO-SVM for identifying systemically important financial institutions

The effective identification of systemically important financial institutions (SIFIs) is key to preventing and resolving systemic financial risks; thus, it is of great research significance for emerging countries to supervise SIFIs and manage systemic financial risks. Since traditional research on identifying SIFIs does not consider emerging machine learning models, it is difficult to properly fit the characteristics of actual financial institutions’ asset distribution. This paper proposes a new method for measuring SIFIs, integrating the PSO-SVM model into the Copula-CoVaR model. This new PSO-SVM-Copula-CoVaR model is meant to evaluate China’s SIFIs based on the publicly traded price data of Chinese listed financial institutions. The empirical results show that, compared with the traditional parameter method (GARCH model) and the nonparametric method (kernel density estimation), the marginal distribution estimation method using the PSO-SVM method can better fit the distribution of an institution’s financial asset return sequence. That is, the model proposed in this paper helps regulatory authorities improve the list of SIFIs more reasonably and implement effective regulatory measures.