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.

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.

Generally, the greatest RISK in the capital market or in the portfolio of investors occurs when large sudden changes occur in the unfavorable portfolio. These losses are in the distribution sequence, and for this purpose they are called "limitative VALUEs". In this research, the logarithmic efficiency of the Tehran Stock Exchange INDEX based on the information received during the time interval between the day (due to the use of high frequency data) during the years 1392 to 1395, and the use of the maximum block approach in measuring the VaR VALUE INDEX is used. It turned out VaR INDEX was calculated using historical simulation methods and variance-covariance method as the traditional RISK assessment criteria and the results were compared. The results of data analysis in R software showed that the use of monthly information in calculating the RISK-weighted VALUE INDEX has a higher predictive accuracy and the error rate (test error) in this case is lower than traditional RISK assessment methods.

Objective: Passive management is an investing strategy that tracks a market VALUE-weighted INDEX or portfolio. It seeks to minimize the cost of investment fees and to avoid undesirable repercussions of the unpredictability of future trends. Active portfolio management tries to beat the market while passive portfolio management pursues a similar RISK-return pattern to that of the market INDEX. INDEX tracking is a passive investment strategy in the stock market that aims to make a portfolio using constituents of an INDEX. It seeks to mimic its behavior without purchasing all of its constituents. This study aimed to track Tehran Exchange Dividend & Price INDEX (TEDPIX). Methods: In this study, portfolios were tracked and their performances were examined by applying a two-tail mixed conditional VALUE-at-RISK model (main model). Optimizing TMCVaR is a linear program that minimizes the upper deviation and the downside deviation from the benchmark INDEX. The investigated sample included the weekly data gathered from 2011/3/21 to 2018/20/3. The data was divided into 26-time frames including 52 in-sample data and 12 out-ofsample data. Results: Statistical tests confirmed the portfolios resulting from the main model were successful in tracking the INDEX. As a result, the investigated model was recognized as capable of tracking the INDEX. However, due to the tracking error and information ratio, the two models were not statistically different. In the present study, the two models showed the same performance in tracking the INDEX. Conclusion: In this study, a linear mathematical programming model was proposed to form INDEX tracking portfolios. The results showed that although the main model was successful in INDEX-tracking it did not outperform the mean absolute deviation model in terms of reduction in tracking error and increasing information ratio.

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.

As a measurement of RISK, VALUE-at-RISK has always been attractive to researchers in the field of RISK management and capital markets. This importance has always led them to develop models to increase the accuracy of VaR estimation. GARCH models are used to estimate conditional variances in parametric approaches of calculating VaR. In the latest developments, Hansen presented the Realized GARCH model using intra-day data. Regarding the recent high fluctuations of the Tehran Stock Exchange INDEX, the use of Realized GARCH models to compute VaR can increase the accuracy of forecasting. In this paper the three different distributions, Normal, T, and GED, are combined with conventional GARCH models, and the new Realized GARCH model. Next, the VaR of the Tehran Stock Exchange INDEX is forecasted using the rolling window sampling method. And finally, the accuracy of predicted VaR has been evaluated and compared using a two-step Backtest method. The outcome of this study indicates that using the new Realized GARCH model in forecasting VaR will tend to result in more accurate estimates, whether at the 5% or 1% level.

The main objective of this paper is to determine the optimum portfolio of the Tehran Stock Exchange with respect to the VALUE at RISK (VaR) INDEX. Daily data are on the shares of 30 active companies traded in the Tehran Stock Exchange with daily expected return above 0.4 percent in 2004. Optimum portfolio is selected subject to the investors' budget, RISK and VaR confidence levels. Results reveal that the higher confidence level of VaR requires more diversified portfolio. Therefore, beginner investors and those with higher degree of RISK aversion should diversify their budget among shares of various companies. Also, the level of investment affects the combination of the selected portfolio. Results also show that the RISK-Return trade off are in favor of RISK averse investors and the change in time length period can also change the optimal portfolio.