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Systemic risk has been one of the most interesting issues in banking and financial literature during the last years, particularly in evaluating its effects on the stability of the whole financial system during crises. Differently from other studies which analyze systemic risk focusing on European countries, we explore the determinant of systemic risk in other regional or continental banking systems, as Latin America. Using the CoVaR approach proposed by Adrian and Brunnermeier (2016), we study the impact of corporate variables on systemic risk on a sample of 30 Latin American banks belonging to seven countries, continuously listed from 2002Q1 to 2015Q4. We investigate the contribution of the corporate variables over different economic periods: the Subprime crisis (2007Q3–2008Q3), the European Great Financial Depression (2008Q4–2010Q2), and the Sovereign debt crisis (2010Q3–2012Q3).

In this paper, we search to evaluate the systemic risk of the Moroccan banking sector. Indeed, we concentrate on the analysis and the evaluation on transverse dimension of the systemic. From this point of view, two approaches were used. First is based on the estimate on value at risk conditional allowing to measure the systemic importance of each banking institution. In addition, the second approach uses the heteroscedasticity models in order to consider the conditional correlations, making it possible, to measure the dependence between the Moroccan banks and with the whole of the financial system. The results obtained with through these two approaches confirm that ATW, BMCI and the BMCE are the most systemic banks in Moroccan banking system and who can initiate a systemic crisis. On another register and by using the conditional correlations of each bank we built an index of systemic risk. Moreover, a macrofinancial model was developed, connecting the index of the systemic risk and the principal macroeconomic variables. This model affirmed that the contagion dimension of systemic risk is procyclical.

In this paper the interconnectedness among financial institutions and the level of systemic risks of four types of Chinese financial institutions are investigated.

By the means of RAS algorithm, the interconnection among financial institutions are illustrated. Different methods, including Linear Granger, Systemic impact index (SII), vulnerability index (VI), CoVaR, and MES are used to measure the systemic risk exposures across different institutions.

The results illustrate that big banks are more interconnected and hold the biggest scales of inter-bank transactions in the financial network. The institutions which have larger size tend to have more connection with others. Insurance and security companies contribute more to the systemic risk where as other institutions, such as trusts, financial companies, etc. may bring about severe loss and endanger the financial system as a whole.

Since other institutions with low levels of regulation may bring about higher extreme loss and suffer the whole system, it deserves more attention by regulators considering the contagion of potential risks in the financial system.

This study builds a valuable contribution by examine the systemic risks from the perspectives of both interconnection and tail risk measures. Furthermore; Four types financial institutions are investigated in this paper.

The financial crisis 2007‐2009 calls for a regulatory response. A crucial element of this task is the treatment of systemic risk. Basel III gains centre stage in this process. Thus, the purpose of this paper is to evaluate Basel III, examining its ability to reduce systemic risk.

The paper highlights the importance of reducing systemic risk to achieve the goal of overall financial stability. By first focusing on the theoretical foundations of systemic risk, this paper explores and analyzes the crucial aspects of this almost impalpable risk type. It further investigates the current regulation of systemic risk, clearly showing Basel II's inability to reduce it. Then, it evaluates the Basel Committee's efforts to address these weaknesses through Basel III by investigating its incentives and its ability to reduce obvious drawbacks of Basel II as well as systemic risk factors.

The findings show that there are still adjustments necessary. Although the development of Basel III is well advanced, providing some stabilizing incentives, there are still issues calling for closer consideration to counter all Basel II drawbacks and systemic risk factors adequately. These include: a risk‐weighted leverage ratio; a more thorough treatment of procyclicality; adjustments for the NSFR (Net Stable Funding ratio); and most importantly, the mandatory issue to internalize negative externalities from financial institutions, that is, the call for pricing systemic risk.

The paper not only examines the new Basel III framework, as a response to the Financial Crisis 2007‐2009, but also draws attention to specific areas which the Basel Committee and regulators need to focus on more thoroughly.

The near‐collapse of the world's financial system in 2008 brought into focus significant limitations in the data and analysis tools available to mitigate potential risks across the financial system. It has raised calls to provide comprehensive data and adequate tools to identify and relieve systemic risk. In this paper, an infrastructure is proposed to address the need for a new information system in systemic regulation.

The proposed infrastructure is developed using the Fed's Bank Holding Company Supervision Manual as a guideline. The model uses a data fusion approach that allows integration of inspection data, external data, and other regulatory data of different granularity. A proprietary application known as Decision Making Toolbox (DMT) is being developed with three‐tier architecture.

The integrated all‐in‐one approach will enhance the efficiency, scope, and quality of studies applied to systemic regulation and will facilitate easy decision making for effective regulation.

This concept integrates data and measures that are needed for systemic regulation. It facilitates easy decision making, by regulators with an integrated all‐in‐one information infrastructure, for effective regulation.

This study investigates the risk-taking behavior of financial institutions in the USA. Specifically, differences between taking risks that affect primarily the shareholders of the institution and risks contributing to the overall systemic risk of the financial sector are examined. Additionally, differences between risk-taking before, during and after the financial crisis of 2007/2008 are examined.

To analyze the determinants of stand-alone and systemic risk, a generalized linear model including size, governance, charter value, business cycle, competition and control variables is estimated. Furthermore, Granger causality tests are conducted.

The results show that systemic risk has a positive effect on valuation and that corporate governance has no significant effect on risk-taking. The influence of competition is conditional on the state of the economy and the risk measure used. Systemic risk Granger-causes idiosyncratic risk but not vice versa.

The major limitations of this study are related to the analyzed subset of large financial institutions and important risk-culture variables being omitted.

The broad policy implication of this paper is that systemic risk cannot be lowered by market discipline due to the moral hazard problem. Therefore, regulatory measures are necessary to ensure that individual financial institutions are not endangering the financial system.

This study contributes to the empirical literature on bank risk-taking in several ways. First, the characteristics of systemic risk and idiosyncratic risk are jointly analyzed. Second, the direction of causality of these two risk measures is examined. Moreover, this paper contributes to the discussion of the effect of competition on risk-taking.

The paper models the financial interconnectedness and systemic risk of shadow banks using Granger-causal network-based measures and takes the Indian shadow bank crisis of 2018–2019 as a systemic event.

The paper employs pairwise linear Granger-causality tests adjusted for heteroskedasticity and return autocorrelation on a rolling window of weekly returns data of 52 financial institutions from 2016 to 2019 to construct network-based measures and calculate network centrality. The Granger-causal network-based measure ranking of financial institutions in the pre-crisis period (explanatory variable) is rank-regressed with the ranking of financial institutions based on maximum percentage loss suffered by them during the crises period (dependent variable).

The empirical result demonstrated that the shadow bank complex network during the crisis is denser, more interconnected and more correlated than the tranquil period. The closeness, eigenvector, and PageRank centrality established the systemic risk transmitter and receiver roles of institutions. The financial institutions that are more central and hold prestigious positions due to their incoming links suffered maximum loss. The shadow bank network also showed small-world phenomena similar to social networks. Granger-causal network-based measures have out-of-sample predictive properties and can predict the systemic risk of financial institutions.

The study considers only the publicly listed financial institutions. Also, the proposed measures are susceptible to the size of the rolling window, frequency of return and significance level of Granger-causality tests.

Supervisors and financial regulators can use the proposed measures to monitor the development of systemic risk and swiftly identify and isolate contagious financial institutions in the event of a crisis. Also, it is helpful to policymakers and researchers of an emerging economy where bilateral exposures' data between financial institutions are often not present in the public domain, plus there is a gap or delay in financial reporting.

The paper is one of the first to study systemic risk of shadow banks using a financial network comprising of commercial banks and mutual funds. It is also the first one to study systemic risk of Indian shadow banks.

The purpose of this paper is to analyze the impact of financial penalties on the stability of the banking sector.

A unique database of 671 financial penalties imposed on 68 international listed banks between 2007 and 2014 and a fixed-effects panel data approach were used.

The results show that financial penalties increase banks’ systemic risk exposure but do not significantly affect banks’ contribution to systemic risk. Additionally, the link between financial penalties and systemic risk exposure is weaker in regulatory and supervisory systems with more prompt corrective power among national authorities. By contrast, supervisory authorities’ stronger power to declare insolvency and a greater external monitoring culture exacerbate the positive effects of financial penalties on systemic risk exposure.

The punishment of misconduct should correct the social harm and prevent future misconduct while ensuring the banking system’s stability. Therefore, authorities should punish misconduct by implementing penalties against the financial institutions at a specific amount that offsets the damages of misconduct but does not threaten systemic stability. Penalties against institutions may be complemented by financial penalties against upper management to induce a more responsible culture in banks.

This paper is the first to study the effect of financial penalties on the stability of the financial system. The results contribute to the ongoing debate on the appropriateness of financial penalties and address the question of whether bank regulators reduce or contribute to banks’ systemic risk.

The purpose of this paper is to measure a single financial institution's contribution to systemic risk by using extremal quantile regression and analyzing the influential factors of systemic risk.

Extreme value theory is applied when measuring the systemic risk of financial institutions. Extremal quantile regression, where extreme value distribution is assumed for the tail, is used to measure the extreme risk and analyze the changes in and dependencies of risk. Furthermore, influential factors of systemic risk are analyzed using panel regression.

The key findings of the paper are that value at risk and contribution to systemic risk are very different when measuring the risk of a financial institution; banks’ contributions to systemic risk are much higher; and size and leverage ratio are two significant and important factors influencing an institution's systemic risk.

Characterizing variables of financial institutions such as size, leverage ratio and market beta should be considered together when regulating and constraining financial institutions.

To take extreme risk into account, this paper measures systemic financial risk using extremal quantile regression for the first time.

This chapter investigates the presence of a difference in the systemic risk level between Islamic and conventional banks in Bangladesh. The authors compare systemic resilience of three types of banks: fully fledged Islamic banks, purely conventional banks (CB), and CB with Islamic windows. The authors use the market-based systemic risk measures of marginal expected shortfall and systemic risk to identify which type is more vulnerable to a systemic event. The authors also use ΔCoVaR to identify which type contributes more to a systemic event. Using a sample of observations on 27 publicly traded banks operating over the 2005–2014 period, the authors find that CB is the least resilient sector to a systemic event, and is the one that has the highest contribution to systemic risk during crisis times.