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.

The general trend to focus more on core competencies has forced companies to use outsourcing strategies and led to appearance of supply chain. Supply chain risks can arise from many sources, including political events, demand fluctuation, technological changes, financial instability and natural disasters. To be able to handle these risks, supply chain risk management is needed. Managing supply risk is an essential part of the supply chain risk management and in this research we focus on supply risk management. We use Value at risk, extreme Value theory and cash flow at risk to present a method for quantification of supply risks such as late delivery, quality risk, natural disasters risk and business risk (financial instability of suppliers). Furthermore, a model for supplier selection and order allocation based on these supply risks, has been proposed. Finally, a numerical example is presented and some computational result is reported.fluctuations of supply chain and market adjustments are considered one of the duties of this ministry. This study will help in removing barriers and presenting new innovative solutions for using scientific approaches based on system analysis in different governmental sectors.

Value-at-risk and Average Value-at-risk are tow important risk measures based on statistical methoeds that used to measure the market's risk with quantity structure.Recently, linear regression models such as least squares and quantile methods are introduced to estimate these risk measures. In this paper, these two risk measures are estimated by using omposite quantile regression. To evaluate the performance of the proposed model with the other models, a simulation study was conducted and at the end, applications to real data set from Iran's stock market are illustarted.

Value at risk (VaR) measures risk exposure at a given probability level and is very important for risk management. In this paper, mainly EVT models are compared to other well-known models such as GARCH, Historical Simulation and Filtered Historical Simulation. Then evaluation their models with different back testing such as Kupiec test, Christoffersen test and Lopez Loss function.Our results indicate that using conditional methods and Extreme Value Theory to forecast Value at risk, is better than other models. And we should examine different methods for forecast Value at risk, then select the best method for any tails of distributions.