Today, Systemic Risk is being analyzed as one of the major issues in financial institutions. Banks are one of the institutions that can be linked to systemic risk based on global experience. Therefore, in the study, we evaluate the systemic risk in the banking system of the country via the marginal expected shortfall (MES) criterion. For the purpose of the present study, 17 banks listed on the Tehran Stock Exchange that had seasonal information required for this research over a period of 1389 to 1397 were selected and the systemic risk in these banks was calculated by MES criterion. The finding of this study show the difference between MES of banks and indicate that if a crisis occurs in the financial system or market, the banks are affected but the drgree of impact is different from the finance crisis. Furthermore, compared with other banks, the estimation of the highest marginal expected shortfall belonged to bank gardeshgari (15. 84) and the lowest belonged to Bank Sarmayeh (-18. 38). In other words, if there is a crisis in the market, Bank Gardeshgari and Bank Sarmayeh are expected to experience a return of 15. 84 percent and-18. 34 percent, respectively.

ObjectiveSystemic risk, with contagion between different parts of the market, causes financial crises and instability in the economy; therefore, it is necessary to recognize, measure, control, and combat systemic risk. Since financial institutions have extensive connections with all institutions and can transfer and spread risk, in the financial literature, the systemic risk of the financial sector has a special place, and most of the research conducted on the systemic risk field has been devoted to the financial industry, while the non-financial sector in this field has received less attention. On the other hand, non-financial corporations (NFCs) are also connected to each other and to the financial sector, and as a result, NFCs have potentially systemic importance. Therefore, considering the importance of systemic risk in the economy's stability and the position of the non-financial sector in the society's economy, the systemic importance of non-financial institutions and industries should be evaluated and explained. Also, knowing the drivers of systemic risk in NFCs helps to identify important systemic NFCs, make economic decisions, and formulate appropriate rules. On the other hand, in portfolio risk management decisions, the individual risk criteria of institutions are often used, and risk transmission between institutions has not been adequately considered. Therefore, for better portfolio risk management, systemic risk should also be included in decisions. According to the mentioned cases, the current research aims to comprehensively examine the systemic risk of the non-financial sector from four different perspectives. For this purpose, the systemic importance of non-financial corporations was investigated, and the corporations were ranked in terms of systemic risk. Also, the systemic importance of various industries in Iran's capital market was investigated, and the ranking of all industries in terms of systemic risk was determined. In addition, firm-level characteristics related to the systemic risk of non-financial corporations were identified. And at the end, the application of systemic risk in portfolio risk management by asset managers, retail investors, and policymakers was explained. MethodsTo measure systemic risk, the Marginal Expected Shortfall criterion (MES) was used and analyses were performed at the firm, industry, and random portfolio levels. For this purpose, 284 financial and non-financial corporations, in addition to the financial industry and non-financial industries, and three separate portfolio groups, with each portfolio group consisting of 100 random portfolios, were examined from 2008 to 2022. The systemic importance of non-financial corporations and industries was determined by comparing the median of MES between non-financial corporations and financial corporations, as well as non-financial industries and the financial industry. Also, by ranking, the most important and least important corporations and industries were determined from a systemic perspective. Additionally, a new estimation method known as 'random effects within-between' (REWB) regression was employed to understand firm-level characteristics associated with MES in non-financial corporations. In REWB regressions, the cross-sectional (between) and longitudinal (within) relationships between each firm characteristic and systemic risk are estimated simultaneously. Finally, to evaluate the effect of the systemic importance of corporations and the weight assigned to systemically important corporations on portfolio risk, the median of downside risk measures was compared between different portfolio groups. ResultsThe findings of the research confirmed the systemic importance of non-financial corporations and non-financial industries. Also, separate analysis results on large corporations in the capital market indicate that the systemic importance of large non-financial corporations is almost equal to the systemic importance of large financial corporations. In addition, during the research period, Isfahan Mobarakeh Steel Company and Damavand Mining Company were found to have the highest and lowest ranks of systemic importance, respectively, among companies. The rubber and plastic industry and the pharmaceutical industry were also identified as having the highest and lowest ranks of systemic importance, respectively. In addition, firm-level characteristics of Beta, Value at Risk, Accounts Receivable, Size, Cash Holding, Dividend, Asset Tangibility, and External Financial Dependence are directly and significantly related to MES. Furthermore, the systemic importance of corporations and the weight assigned to systemically important corporations significantly affect the downside risk criteria of the investment portfolio. ConclusionTo maintain economic stability, the systemic risk of the non-financial sector should be considered, as well as that of the financial sector. For example, knowing the drivers of systemic risk in non-financial companies can help to control systemic risk. Also, retail investors, portfolio managers, and regulators of mutual funds, by including the systemic risk of all companies in their decisions, can achieve better portfolio risk management.

In this research, it is attempted to present a framework for estimating and predicting systemic risk in Iran capital market using the marginal expected shortfall approach (MES), which has recently been considered in systemic risk literature. On this basis, MES as a systemic risk measure, will be analyzed in terms of assumptions for market and firm returns as a function of mean, volatility, correlation, and tail expectations and its components will be measured using an ARMA-GJR-GARCH-DCC framework and a nonparametric tail expectation estimator. In this way, a weekly panel will be created from the company's MES. On the other hand, the systemic risk is built up in a period that looks calm and low fluctuations, and is accumulated until activation. In other words, systemic risk potential increases as fluctuations decrease. In this study, it was attempted to predict systemic risk by taking advantage of the panel structure of the data and the relationship between MES and firm-specific variables that are available in certain sections.

After the 2008 financial crisis, the importance of studying systemic risk became more apparent. In this regard, various metrics have been presented to measure systemic risk, but the main question is which metric has a better and more comprehensive function than other metrics. The main contribution of this paper is answer foresaid question. In this research, the most widely used indicators include systemic expected shortfall, marginal expected shortfall, delta conditional value at risk and component expected shortfall using data related to 38 companies (selected sample) listed in the Tehran Stock Exchange (TSE) from 2014 to 2023 (a ten-year period) has been examined and compared. The research method is based on the correlation and reliability matrix of the values of each measure and the ranking of the sample companies during the period under review. The findings show that the measure of delta Conditional Value at Risk (∆CoVaR) has the best performance in order to explain systemic risk.

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.

Systemic risk has been considered as one of the new concepts in the field of finance since 2008 and this risk is more considered in the banking industry due to the close relationship between banks in their daily operations. Therefore, identifying the factors affecting this risk in the banking industry is the main purpose of this study. In this study, the relationship between macroeconomic indicators (interest rate, economic growth rate and inflation), risk (liquidity and default risk) and competitiveness (Herfindahl-Hirschman index and asset size) using banks' data from 2009 to date and with The GMM data panel method was tested. The results of modeling show that there is a significant and direct relationship between default risk index (credit) and systemic risk of the banking industry. Also, in all competition indicators, including Herfindahl-Hirschman index and the size of banks, there is a direct relationship, and in macroeconomic indicators, the relationship between interest rates and inflation with the systemic risk of banks is direct and significant.

Value at risk and expected shortfall are the two most popular measures for calculating financial risk. To calculate these measures (Value at risk and expected shortfall) there are many approaches, which can be divided into two main categories; parametric and non-parametric. In parametric approach it is supposed that the distribution of asset return belongs to a specific class of distributions. For some distributions we can claculate easily the mentioned measures. In this paper the the relation of epected shortfall has been proved for four symetric distribution.