The use of variance as a Risk measure is limited by its non-coherentnature. On the other hand, standard deviation has been demonstrated as acoherent and effective measure of market volatility. This paper suggests theuse of standard deviation in portfolio optimization problems with cardinalityconstraints and short selling, specifically in the mean-Conditional Value-at Riskframework. It is shown that, subject to certain conditions, this approach leadsto lower standard deviation. Empirical results obtained from experiments onthe SP index data set from 2016-2021 using various numbers of stocks andconfidence levels indicate that the proposed model outperforms existing modelsin terms of Sharpe ratios.

Initially In this study, we introduce a new index called Revised Sharp (R-Sharp) for evaluation of portfolio in investment companies in Tehran Stock Exchange, and then examine this index were compared with the Sharp index. In the R-Sharp index, Value at Risk concept was used due to the properties of VaR and its application in the international financial institutions.The results indicate that the VaR calculation by GARCH is not applicable since time series data have not heteroscedasticity. Therefore, VaR was calculated for 10 investment companies by Risk Metrics method with  l=0.94 in coefficient level at 99.9%, 99% and 95% for 1-day and 10-day. In order to assess the accuracy of VaR calculation, the Wilcoxon signed ranks test was utilized. The results indicate that VaR Back testing at 95% and one-day period for all companies, were reliable.In this study, after calculating VaR and VaR Back testing, R-SHARP and SHARP indexes calculated for the period of study (2007-2010). The results show that there are some differences in the ranking of R-SHARP and SHARP indexes. So we tested the difference of R-SHARP and SHARP indexes by nonparametric tests such as Wilcoxon signed ranks test. Results of these tests indicate that sleight insignificant differences of indexes.

Initially In this study, we introduce a new index called Revised Sharp (R-Sharp) for evaluation of portfolio in investment companies in Tehran Stock Exchange, and then examine this index were compared with the Sharp index. In the R-Sharp index, Value at Risk concept was used due to the properties of VaR and its application in the international financial institutions.The results indicate that the VaR calculation by GARCH is not applicable since time series data have not heteroscedasticity. Therefore, VaR was calculated for 10 investment companies by Risk Metrics method with  l=0.94 in coefficient level at 99.9%, 99% and 95% for I-day and 10-day. In order to assess the accuracy of VaR calculation, the Wilcoxon signed ranks test was utilized. The results indicate that VaR Backtesting at 95% and one-day period for all companies, were reliable.In this study, after calculating VaR and VaR Backtesting, R-SHARP and SHARP indexes calculated for the period of study (2007-2010). The results show that there are some differences in the ranking of R-SHARP and SHARP indexes. So we tested the difference of R-SHARP and SHARP indexes by nonparametric tests such as Wilcoxon signed ranks test. Results of these tests indicate that sleight insignificant differences of indexes.

In this paper portfolio selection problem with interval optimization approach is surveyed. CVaR is Risk measure. CVaR is the expected loss depending on the chosen confidence level. Using CVaR makes the portfolio selection problem linear programming. Contribution of this paper is to consider mean expected interval; this development help portfolio selection problem to consider uncertainty. Interval optimization is modeling approach to consider parameters uncertainty in this paper. Considering uncertainty make model more realistic. The results of model show that this approach has computational efficiency and on the other hand proposed model produce better solution in Risk and portfolio rate of return point of view.

Portfolio selection,in order to maximize the profit from investment,is an important concern for minor and institutional investors.Therefore;efficient and secure optimization of financial assets is one of the most important new and modern,financial topics,trying to improve the portfolio performance using modern approaches of other sciences.Accordingly,this article aimed to optimize the index returns of top 10 companies of Tehran Stock Exchange from 2011 to 2015 using portfolio Risk minimization approach with the maximum yield according to Conditional Value at Risk and differential evolution algorithm(DE-CVaR) on a monthly basis.The results showed that differential evolution algorithm with the Conditional Value at Risk approach,had better Sharpe and returns ratios by CVaR Value compared to the random algorithm.The results of posttest with monthly approach also showed that DE-CVaR was better than random algorithm in terms of the criteria for selecting the optimal portfolio.

Systematic Risk (in economics often called aggregate Risk or undiversifiable Risk) is vulnerability to events which affect aggregate outcomes such as broad market returns, total economy-wide resource holdings, or aggregate income. In many contexts, events like earthquakes and major weather catastrophes pose aggregate Risks they affect not only the distribution but also the total amount of resources. In this paper we study systemic Risks in the Iranian banking sector by using two famous systemic Risk measures theMES (marginal expected shortfall) and CoVaR. To compute both measures we employ Engle's dynamic Conditional correlation model. Our empirical analysis shows, first, that although these two systemic Risk measures differ in defining the contributions to systemic Risk, both are qualitatively very similar in explaining the cross-sectional differences in systemic Risk contributions across banks. Last, using a threshold VAR model, we suggest an overall systemic Risk measure – the aggregateMES – and its associated threshold Value for use as an early warning indicator. The paper is innovative in terms of the use of statistical models (dynamic Conditional correlation model) and available data Looking for the rating of commercial banks using by approaches MES and COVAR.

This paper presents a mixed integer linear program (MILP) model as well as a solution method based on the Benders decomposition algorithm for optimal design of sustainable Energy Hub (EH). A modeling framework to consider environmental (Env) and social (Soc) impacts of the EH's components is incorporated to achieve sustainable EH. First, the life cycles of different components are analyzed in order to determine Env and Soc impacts. Then, the EH design model is developed by using scenarios-based stochastic programming integrating Conditional Value-at-Risk (CVaR) in its objective function as a Risk criterion to deal with uncertain nature of parameters. Benders decomposition (BD) algorithm is used to decompose the original problem in order to address heavy computational burden of the problem, especially when a large number of scenarios is used to properly represent uncertainties. The results shows effectiveness of the proposed BD to handle large problem sizes compared to the CPLEX solver and indicates that taking the external cost into account resulted in higher renewable Distributed Energy Resources (DERs) in the optimal configuration, which have lower negative Env and Soc impacts. Also, strength of stochastic programming in handling data uncertainty and controlling Risk level is investigated.