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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.

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.

This study investigates how this pandemic impacted the systemic risk in Indonesia’s Islamic commercial banks (ICBs) and conventional commercial banks (CCBs). The authors use quantitative methods, and systemic risk is measured using value at risk (VaR) and Conditional Value at Risk (CoVaR). This study provides empirical evidence regarding the estimation and determination of systemic risk. By using spillover measures, the authors find a significant increase in systemic risk among the sample banks. The novelty in this research is the measurement of the level of banking risk in the dual banking system in Indonesia. This study makes profound contributions to the literature and suggests various policy recommendations, including identifying essential institutions and testing the benefits of policy responses in containing systemic risk. These findings need to be considered by the government and financial authorities in making accurate regulations and policies.

We employ three systemic risk measures of banks, including the systemic risk index (SRISK) and marginal expected shortfall (MES) of Brownlees and Engle (2017) and the conditional Value-at-Risk (ΔCoVaR) of Adrian and Brunnermeier (2016), to analyze bank's exposure and contribution to systemic risk in the banking system when a financial crisis occurs. We find evidence that time-varying systemic risk exists, and systemic risk exposures escalate with the interconnectedness of banks. We also find revenue diversification is another significant factor that reduces a bank's exposure to systemic risk but not for banks in Taiwan and Singapore.

A systemic risk measure is proposed accounting for links and mutual dependencies between financial institutions utilizing tail event information. Financial Risk Meter (FRM) is based on least absolute shrinkage and selection operator quantile regression designed to capture tail event co-movements. The FRM focus lies on understanding active set data characteristics and the presentation of interdependencies in a network topology. Two FRM indices are presented, namely, FRM@Americas and FRM@Europe. The FRM indices detect systemic risk at selected areas and identify risk factors. In practice, FRM is applied to the return time series of selected financial institutions and macroeconomic risk factors. The authors identify companies exhibiting extreme “co-stress” as well as “activators” of stress. With the SRM@EuroArea, the authors extend to the government bond asset class, and to credit default swaps with FRM@iTraxx. FRM is a good predictor for recession probabilities, constituting the FRM-implied recession probabilities. Thereby, FRM indicates tail event behavior in a network of financial risk factors.

This chapter proposes a measure of systemic default interconnectedness between banks, non-banks, housing finance companies in India and globally systemically important banks based on variance decompositions associated with a multiple variable vector autoregression of probability of default of the institutions. We call it the “vulnerability spillover index” (VSI). The vulnerability indices capture all the major macro and financial stress events in the Indian and global economy explaining the interconnections between sectors and underlying reasons for spillovers and potential for a systemic crisis. Thresholds of VSI are calculated which may enable prediction of financial stress events.

The latest financial crisis has stressed the need of understanding the world financial system as a network of interconnected institutions, where financial linkages play a fundamental role in the spread of systemic risks. In this paper we propose to enrich the topological perspective of network models with a more structured statistical framework, that of Bayesian Gaussian graphical models. From a statistical viewpoint, we propose a new class of hierarchical Bayesian graphical models that can split correlations between institutions into country specific and idiosyncratic ones, in a way that parallels the decomposition of returns in the well-known Capital Asset Pricing Model. From a financial economics viewpoint, we suggest a way to model systemic risk that can explicitly take into account frictions between different financial markets, particularly suited to study the ongoing banking union process in Europe. From a computational viewpoint, we develop a novel Markov chain Monte Carlo algorithm based on Bayes factor thresholding.

With the help of financial engineering – and equipped with the modern technique of risk management – securitisation was supposed to identify and evaluate risks and parcel them out to informed parties who could bear them. In hindsight, we can see that this somewhat simplistic thesis – espoused by market participants as well as the academic promoters of modern techniques of risk management – seemed to promise a great deal more than it could ultimately deliver. At this juncture, however, the danger of regulatory over-reaction – which might be throwing the baby (financial innovation) out with the bath-water (overlooking/under-pricing of risk) – is very real and (in my view) calls for policy measures of this sort should be resisted firmly not only by market participants but also by regulators. This is not to say that regulation should be seen as immune from responsibility in the unfolding of the current credit crisis (quite the opposite would more likely be closer to the truth). As we shall see below (Section “Financial Crisis and Credit Ratings Debacle in SF”), the best risk-management practices – and related tools available before the crisis – provided enough ammunition to caution against the uncertainty surrounding risk assessment for some categories of SF products. However, the increasing complexity embedded in an increasing number of deals did provide genuine new challenges even to best risk-management practices.