Sharif Digital Repository

Value at risk of an asset, is the asset’s expected maximum loss for a certain period of time and at a specified confidence level. Value-at-Risk can be calculated in the bank with its inter-nal method or standardized method. when a method have more violation number then bank need to keep more daily capital requirements. under the Basel 2 agreement if the violation of method more than 10 times in year, the Bank uses the standardized method.
There are trade off Between daily capital charge and violations. Therefore, existing methods for calculating the value at risk, usually lead to much daily capital charge or many violations. Studies show with combination of different methods to calculate... 

This study intends to evaluate Value at Risk of the Gold price returns for Short and Long positions by using Extreme value, Normal and t-student parametric model approaches. Conditional variance also is estimated by GARCH(1,1), FIGARCH, FIEGARCH, PGARCH, EGARCH and TGARCH with the three window sizes: 1000, 750 and 500. Accuracy and sufficiency of specified models are tested in two stages. In the first stage we select the models which are sufficient and in the second stage, a loss function is calculated for the VaR methods which passed the sufficiency tests. Our result shows that the accuracy ranking is not independent on window size. Nevertheless, the most accurate models are those which use... 

Multi-period Value-at-Risk (VaR) forecasting is important for risk management. Financial institutions especially commercial banks according to Basel Committee regulations for capital adequacy in “Internal Approach”, should forecast their VaR for multi-period horizons (longer than one day). The most conventional approach for forecasting multi-period VaR is scaling one-day forecasted VaR that is called “square root of time rule”; therefore most works in this area have been focused on forecasting one-day VaR. In this study, we review performance of a wide range of different methods in forecasting multi-period VaR. The results of our study show that historical simulation performs weakly in... 

Value-at-Risk (VaR) is the most commontool for risk management. This tool is used to measure market risk and also used as a basis in determining financial standards for international financial institutions. VaR is the maximum loss of the asset portfolio at the specified confidence level and certain time horizon. Many parametric, nonparametric and semi parametric methods have been invented for VaR estimation. Each one of these methods has its advantages and disadvantages and different methods may perform better in differnet situations.When estimating VaR, we can choose one of these methods or we can combine the VaRs estimated by different methods. There are few researches conducted on VaR... 

Along with the development of electronic exchanges, accessibility to various data streams, increasing computing power, decreasing trading costs, and growing competition in financial investment industry, quantitative trading strategies or quantitative trading rules have developed rapidly in the recent decades. These strategies try to forecast the future price movements of risky assets from the historical market information in algorithmic ways or statistical ways and thus challenge the Efficient Market Hypothesis.
The increasing attention to these strategies and lack of related empirical studies in the financial markets of Iran, motivate the research in this area. Furthermore, despite its... 

This thesis proposes two methods for backtesting VaR models. The first is the combination of saddlepoint technique with Berkowitz backtesting and the second is based on maximum loss which uses Fischer-Tippet theorem to backtest VaR models. Monte Carlo simulation studies show that the power of these new backtests, especially the latter which is easy to use, is not less than complex Backtests that are well-known for their accuracy