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.

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.

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.

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.

Objective The simplest thing that may make an amateur investor invest in a fund is simply to look at the fund’ s return that can be calculated very easily. Capital market experts have always tried to make investors aware of the threat of making judgments merely based on the fund's returns. Methods Accordingly, the present study addresses one of the performance evaluation techniques using risk assessment models. This technique simply focuses on the impact of the capital market on a mutual fund that can largely reflect the position of asset management and the stability of the fund’ s performance. This study employs LTD, SES, (MES), CoVaR models to assess systemic risks in equity funds in Iran. Besides, the TOPSIS model and a combination of the mentioned techniques are used to rank each mutual fund in terms of systemic risk. Results According to the topic of the paper, designing a model for ranking the mutual funds is finally presented based on the models based on risk assessment based on systemic risk assessment based on the criteria presented in the Iranian stock with regard to the criteria presented by this ranking. Conclusion Using the hybrid regression analysis method (quantile)-topsis, the risk of a system of four aspects of value at risk (CoVaR), the Marginal loss of Expected loss ((MES)) and the lower tail dependence (LTD) in the funds were evaluated and the maximum impact on the least impact of funds in the system was determined.

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.

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.

The present study compares the performance of the long memory FIGARCH model, with that of the short memory GARCH specification, in the forecasting of multi-period value-at-risk and Expected Shortfall across 3 industry indices in Tehran Stock Exchange such as chemical, vehicle and metals. The dataset is composed of daily data covering the period from May, 2011 to May, 2015. According to the result of this research accounting for fractional integration in the conditional variance model does not appear to improve the accuracy of the VaR forecasts for the 1-day-ahead, 10-day-ahead and 20-day-ahead forecasting horizons relative to the short memory GARCH specification. Furthermore, the GARCH model has a lower quadratic loss between actual returns and ES forecasts, for the majority of the indices considered in 1-day, 10-day and 20-day forecasting horizons. Therefore, a long memory volatility model compared to a short memory GARCH model does not appear to improve the VaR and ES forecasting accuracy, even for longer forecasting horizons.