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We examine the systemic risk (SR) of listed Islamic banks (IB) around the world for the 2005–2020 period. Our research hypothesis states that if IB are resilient to economic crises, we expect a stable pattern of SR for IB across pre- and during international crises. Our empirical results do not confirm this hypothesis. We find evidence that the SR of IB has increased sharply during the subprime and COVID-19 crises. Furthermore, we identify the presence of systematically important Islamic banks (SIIB). These results are robust to different measures of SR and do not depend on a specific region. These findings suggest that the Islamic banking system is vulnerable to downside risk and to the failure of SIIB. [ABSTRACT FROM AUTHOR]

Expectiles define the only law-invariant, coherent and elicitable risk measure apart from the expectation. The popularity of expectile-based risk measures is steadily growing and their properties have been studied for independent data, but further results are needed to establish that extreme expectiles can be applied with the kind of dependent time series models relevant to finance. In this article we provide a basis for inference on extreme expectiles and expectile-based marginal expected shortfall in a general β-mixing context that encompasses ARMA and GARCH models with heavy-tailed innovations. Our methods allow the estimation of marginal (pertaining to the stationary distribution) and dynamic (conditional on the past) extreme expectile-based risk measures. Simulations and applications to financial returns show that the new estimators and confidence intervals greatly improve on existing ones when the data are dependent. [ABSTRACT FROM AUTHOR]

This paper proposes a novel methodology to calibrate the magnitude of the countercyclical capital buffer (CCyB) using market‐based stress tests. The macroprudential authority in our paper aims to contain the possibility of a breach of a minimum capital ratio in the event of a severe system‐wide shock within a certain permissible failure probability. We apply the methodology by stress‐testing major banks in six advanced economies on a quarterly basis over a period of 27 years. The estimates suggest that the cap on the CCyB should not be less than around 1.7% of total assets. Its potential normal‐times level is estimated at approximately 0.8% of total assets. [ABSTRACT FROM AUTHOR]

Under minimal assumptions, we prove that an empirical estimator of the tail conditional allocation (TCA), also known as the marginal expected shortfall, is consistent. Examples are provided to confirm the minimality of the assumptions. A simulation study illustrates the performance of the estimator in the context of developing confidence intervals for the TCA. The philosophy adopted in the present paper relies on three principles: easiness of practical use, mathematical rigor, and practical justifiability and verifiability of assumptions. [ABSTRACT FROM AUTHOR]

The purpose of this study is to explain the Systemic Risk Model with Marginal Expected Shortfall Approach (MES) as regards the banks listed on the Tehran Stock Exchange. The research population includes 15 banks that were present in Tehran Stock Exchange or Iran’s Over-The-Counter (OTC) for the period 2013 to 2018. Data analysis showed that according to the MES criterion, systemic risk has been declining in the period under review. However, the developments of this index can be divided into two sub-periods 2013-2015 and 2016-2018. In the first period (2013-2015), the level of systemic risk based on this criterion was significantly higher than the level of systemic risk in the second period (2016-2018); Nonetheless, over the time, in the second sub-period, on average, the values amounted to about half of what they were in the first-period level.

Objective: The systemic risk is the risk of a crisis in the financial sector and its transmission to the economy. Due to the importance of social damage caused by the financial crisis, it is necessary to pay attention to the systemic risk and its factors. The purpose of the present study is to investigate the effects of strength of corporate governance mechanisms on systemic risk for financial institutions listed on Tehran Stock Exchange. Methods: In order to study the subject, after extracting the data of 42 financial institutions listed in the Tehran Stock Exchange during the period 1390-1394, combined data and multivariate regression model are used to test the research hypotheses. The strength of corporate governance is scored by applying TOPSIS technique based on the five criteria that as follows: percentage of institutional ownership, major shareholders and managerial investors, board size and the percentage of non-executive members of the board. The systemic risk is measured bases on the marginal expected shortfall (MES) and the expected shortfall of capital (SRISK). Results: The effects of strength of corporate governance mechanisms on (MES) and (SRISK) as two indicators of systemic risk is not accepted, because it has a significant level above 5%. Also, the significant level of control variables (Size) and (Capital Ratio) indicates that larger financial institutions (with higher assets) and higher capital ratio, have greater role in the systemic risk. Conclusion: The research findings show that the strength of corporate governance mechanisms does not have significant effect on the financial institutions' systemic risk.

In the context of bivariate random variables Y (1) , Y (2) , the marginal expected shortfall, defined as E Y (1) | Y (2) ≥ Q 2 (1 − p) for p small, where Q2 denotes the quantile function of Y(2), is an important risk measure, which finds applications in areas like, e.g., finance and environmental science. Our paper pioneers the statistical modeling of this risk measure when the random variables of main interest Y (1) , Y (2) are observed together with a random covariate X, leading to the concept of the conditional marginal expected shortfall. The asymptotic behavior of an estimator for this conditional marginal expected shortfall is studied for a wide class of conditional bivariate distributions, with heavy-tailed marginal conditional distributions, and where p tends to zero at an intermediate rate. The finite sample performance is evaluated on a small simulation experiment. The practical applicability of the proposed estimator is illustrated on flood claim data. [ABSTRACT FROM AUTHOR]

We study the asymptotic behavior of the marginal expected shortfall when the two random variables are asymptotic independent but positively associated, which is modeled by the so‐called tail dependent coefficient. We construct an estimator of the marginal expected shortfall, which is shown to be asymptotically normal. The finite sample performance of the estimator is investigated in a small simulation study. The method is also applied to estimate the expected amount of rainfall at a weather station given that there is a once every 100 years rainfall at another weather station nearby. [ABSTRACT FROM AUTHOR]

To understand and predict chronological dependence in the second‐order moments of asset returns, this paper considers a multivariate hysteretic autoregressive (HAR) model with generalized autoregressive conditional heteroskedasticity (GARCH) specification and time‐varying correlations, by providing a new method to describe a nonlinear dynamic structure of the target time series. The hysteresis variable governs the nonlinear dynamics of the proposed model in which the regime switch can be delayed if the hysteresis variable lies in a hysteresis zone. The proposed setup combines three useful model components for modeling economic and financial data: (1) the multivariate HAR model, (2) the multivariate hysteretic volatility models, and (3) a dynamic conditional correlation structure. This research further incorporates an adapted multivariate Student t innovation based on a scale mixture normal presentation in the HAR model to tolerate for dependence and different shaped innovation components. This study carries out bivariate volatilities, Value at Risk, and marginal expected shortfall based on a Bayesian sampling scheme through adaptive Markov chain Monte Carlo (MCMC) methods, thus allowing to statistically estimate all unknown model parameters and forecasts simultaneously. Lastly, the proposed methods herein employ both simulated and real examples that help to jointly measure for industry downside tail risk. [ABSTRACT FROM AUTHOR]

For two risks, X and Y, the Marginal Expected Shortfall (MES) is defined as 𝔼[Y∣X>FX←(1−p)], where FX is the distribution function of X and p is small. In this paper we establish consistency and asymptotic normality of an estimator of MES on assuming that (X, Y ) follows a Conditional Extreme Value (CEV) model. The theoretical findings are supported by simulation studies. Our procedure is applied to some financial data. [ABSTRACT FROM AUTHOR]

In this paper, a dynamic mixture copula model is used to estimate the marginal expected shortfall in the South African insurance sector. We also employ the generalized autoregressive score model (GAS) to capture the dynamic asymmetric dependence between the insurers’ returns and the stock market returns. Furthermore, the paper implements a ranking framework that expresses to what extent individual insurers are systemically important in the South African economy. We use the daily stock return of five South African insurers listed on the Johannesburg Stock Exchange from November 2007 to June 2020. We find that Sanlam and Discovery contribute the most to systemic risk, and Santam turns out to be the least systemically risky insurance company in the South African insurance sector. Our findings defy common belief whereby only banks are systemically risky financial institutions in South Africa and should undergo stricter regulatory measures. However, our results indicate that stricter regulations such as higher capital and loss absorbency requirements should be required for systemically risky insurers in South Africa.

Balance-sheet indicators may reflect, to a great extent, bank fragility. This inherent relationship is the object of theoretical models testing for balance-sheet vulnerabilities. In this sense, we aim to analyze whether systemic risk for a sample of US banks can be explained by a series of balance-sheet variables, considered as proxies for bank liquidity for the 2004:1–2019:1 period. We first compute Marginal Expected Shortfall values for the entities in our sample and then imbed them into a Random Forest regression setup. Although we discover that feature importance is rather bank-specific, we notice that cash and available-for-sale securities are the most relevant factors in explaining the dynamics of systemic risk. Our findings emphasize the need for heightened prudential regulation of bank liquidity, particularly in what concerns cash and immediate liquidity instrument weights. Moreover, systemic risk could be consistently tamed by consolidating bank emergency liquidity provision schemes.

Abstract Gaussian random vectors exhibit the loss of dimension phenomenon, which relates to their joint survival tail behavior. Besides, the fact that the components of such vectors are light-tailed complicates the approximations of various multivariate risk measures significantly. In this contribution we derive precise approximations of marginal mean excess, marginal expected shortfall and multivariate conditional tail expectation of Gaussian random vectors and highlight links with conditional limit theorems. Our study indicates that similar results hold for elliptical and Gaussian like multivariate risks. [ABSTRACT FROM AUTHOR]

Following the 2007–2008 financial crisis, advanced risk measures were proposed with the specific aim of quantifying systemic risk, since the existing systematic (market) risk measures seemed inadequate to signal the collapse of an entire financial system. The paper aims at comparing the systemic risk measures and the earlier market risk measures regarding their predictive ability toward the failure of financial companies. Focusing on the 2007–2008 period and considering 28 large US financial companies (among which nine defaulted in the period), four systematic and four systemic risk measures are used to rank the companies according to their risk and to estimate their relationship with the company's failure through a survival Cox model. We found that the two groups of risk measures achieve similar scores in the ranking exercise, and that both show a significant effect on the time-to-default of the financial institutions. This last result appears even stronger when the Cox model uses, as covariates, the risk measures evaluated one, three and six months before. Considering this last case, the most predictive risk measures about the default risk of financial institutions were the Expected Shortfall, the Value-at-Risk, the CoVaR and the SES. We contribute to the literature in two ways. We provide a way to compare risk measures based on their predictive ability toward a situation, the company's failure, which is the most catastrophic event for a company. The survival model approach allows to map each risk measure in terms of probability of default over a given time horizon. We note, finally, that although focused on the Great Recession in US, the analysis can be applied to different periods and countries. [ABSTRACT FROM AUTHOR]

Summary: We use tail expectiles to estimate alternative measures to the value at risk and marginal expected shortfall, which are two instruments of risk protection of utmost importance in actuarial science and statistical finance. The concept of expectiles is a least squares analogue of quantiles. Both are M‐quantiles as the minimizers of an asymmetric convex loss function, but expectiles are the only M‐quantiles that are coherent risk measures. Moreover, expectiles define the only coherent risk measure that is also elicitable. The estimation of expectiles has not, however, received any attention yet from the perspective of extreme values. Two estimation methods are proposed here, either making use of quantiles or relying directly on least asymmetrically weighted squares. A main tool is first to estimate large values of expectile‐based value at risk and marginal expected shortfall within the range of the data, and then to extrapolate the estimates obtained to the very far tails. We establish the limit distributions of both of the resulting intermediate and extreme estimators. We show via a detailed simulation study the good performance of the procedures and present concrete applications to medical insurance data and three large US investment banks. [ABSTRACT FROM AUTHOR]

The aim of this paper is to propose a new theoretical framework for analyzing the systemic risk using the marginal expected shortfall (MES) and its correlation-based minimum spanning tree (MST). At first, we develop two parametric models of MES with their closed-form solutions based on the Capital Asset Pricing Model. Our models are derived from the non-symmetric quadratic form, which allows them to consolidate the non-linear relationship between the stock and market returns. Secondly, we discover the evidences related to the utility of our models and the possible association in between the non-linear relationship and the emergence of severe systemic risk by considering the US financial system as a benchmark. In this context, the evolution of MES also can be regarded as a reasonable proxy of systemic risk. Lastly, we analyze the structural properties of the systemic risk using the MST based on the computed series of MES. The topology of MST conveys the presence of sectoral clustering and strong co-movements of systemic risk leaded by few hubs during the crisis. Specifically, we discover that the Depositories are the majority sector leading the connections during the Non-Crisis period, whereas the Broker-Dealers are majority during the Crisis period. [ABSTRACT FROM AUTHOR]

In this article, a copula-based model is proposed to estimate the marginal expected shortfall. The model is based on a dynamic mixture copula. The proposed model captures time-varying nonlinear dependence, which is assumed to be constant in alternative approaches. The time-varying copula parameters are endowed with generalized autoregressive score dynamics. For the institutions of the Dow Jones Industrial Average, several variations of the proposed model are considered and compared with alternative, competing models. It is shown that the proposed model outperforms standard benchmarks and produces reasonable findings regarding the risk contributions of the sectors of the Dow Jones Industrial Average. [ABSTRACT FROM AUTHOR]

This paper compares four commonly used systemic risk metrics using data on U.S. financial institutions over the period 2005–2014. The four systemic risk measures examined are the (i) marginal expected shortfall, (ii) codependence risk, (iii) delta conditional value at risk, and (iv) lower tail dependence. Our results demonstrate that the alternative measurement approaches produce very different estimates of systemic risk. Furthermore, we show that the different systemic risk metrics may lead to contradicting assessments about the riskiness of different types of financial institutions. Overall, our findings suggest that systemic risk assessments based on a single risk metric should be approached cautiously. [ABSTRACT FROM AUTHOR]

The literature proposes several stock return-based measures of systemic risk but does not include a classical hypothesis tests for detecting systemic risk. Using a joint null hypothesis of Gaussian returns and the absence of systemic risk, we develop a hypothesis test statistic to detect systemic risk in stock returns data. We apply our tests on conditional value-at-risk ( CoVaR) and marginal expected shortfall ( MES) estimates of the 50 largest US financial institutions using daily stock return data between 2006 and 2007. The CoVaR test identifies only one institution as systemically important while the MES test identifies 27 firms including some of the financial institutions that experienced distress in the past financial crisis. We perform a simulation analysis to assess the reliability of our proposed test statistics and find that our hypothesis tests have weak power, especially tests using CoVaR. We trace the power issue to the inherent variability of the nonparametric CoVaR and MES estimators that have been proposed in the literature. These estimators have large standard errors that increase as the tail dependence in stock returns strengthens. [ABSTRACT FROM AUTHOR]

This article empirically tests the ‘too non-traditional to fail’ argument by exploring the determinants of systemic risk for the BRICs banks. The argument is found not applicable for the BRICs banks, since the systemic risk is merely determined by balance-sheet characteristics, rather than non-traditional activities such as interbank exposures and off-balance commitment. In contrast, systemic risk for banks from the developped countries is related with both aspects. [ABSTRACT FROM PUBLISHER]

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In the analysis of systemic risk, Marginal Expected Shortfall (MES) may be considered to evaluate the marginal impact of a single stock on the market Expected Shortfall (ES). These quantities are generally computed using log-returns, in particular when there is also a focus on returns conditional distribution. In this case, the market log-return is only approximately equal to the weighed sum of equities log-returns. We show that the approximation error is large during turbulent market phases, with a subsequent impact on MES. We then suggest how to improve the evaluation of MES by means of a second-order approximation. [ABSTRACT FROM AUTHOR]

In this paper, a dynamic mixture copula model is used to estimate the marginal expected shortfall in the South African insurance sector. We also employ the generalized autoregressive score model (GAS) to capture the dynamic asymmetric dependence between the insurers' returns and the stock market returns. Furthermore, the paper implements a ranking framework that expresses to what extent individual insurers are systemically important in the South African economy. We use the daily stock return of five South African insurers listed on the Johannesburg Stock Exchange from November 2007 to June 2020. We find that Sanlam and Discovery contribute the most to systemic risk, and Santam turns out to be the least systemically risky insurance company in the South African insurance sector. Our findings defy common belief whereby only banks are systemically risky financial institutions in South Africa and should undergo stricter regulatory measures. However, our results indicate that stricter regulations such as higher capital and loss absorbency requirements should be required for systemically risky insurers in South Africa. [ABSTRACT FROM AUTHOR]

Balance-sheet indicators may reflect, to a great extent, bank fragility. This inherent relationship is the object of theoretical models testing for balance-sheet vulnerabilities. In this sense, we aim to analyze whether systemic risk for a sample of US banks can be explained by a series of balance-sheet variables, considered as proxies for bank liquidity for the 2004:1–2019:1 period. We first compute Marginal Expected Shortfall values for the entities in our sample and then imbed them into a Random Forest regression setup. Although we discover that feature importance is rather bank-specific, we notice that cash and available-for-sale securities are the most relevant factors in explaining the dynamics of systemic risk. Our findings emphasize the need for heightened prudential regulation of bank liquidity, particularly in what concerns cash and immediate liquidity instrument weights. Moreover, systemic risk could be consistently tamed by consolidating bank emergency liquidity provision schemes. [ABSTRACT FROM AUTHOR]

• Sectoral contributions to systemic risk in the Chinese stock market are estimated. • Weights matter in analyzing sectoral risk contributions. • The top risk contributors are Financials, Industrials and Energy sectors. • Sectoral risk contributions evolve over time. This paper investigates the question of how much each sector contributes to systemic risk in the Chinese stock market. Based on two recently developed approaches, namely, Marginal Expected Shortfall (MES) and Component Expected Shortfall (CES), the empirical results demonstrate that weights of sectors matter. Moreover, Financials, Industrials and Energy sectors are found to be the top risk contributors, though their contributions tend to evolve over time. The results have strong implications to both investors and regulators for risk management and regulatory purposes. [ABSTRACT FROM AUTHOR]