نوع مقاله : پژوهشی
دانشکده مهندسی صنایع، دانشگاه صنعتی مالک اشتر
عنوان مقاله [English]
Developing of capital markets and decreasing of interest rates in commercial banks has caused to stock investing become as one of the most important opportunities to earn returns for individuals and firms. Since, the nature of capital markets is involved to abrupt shocks and volatility we have to allow risk. So, It must be predicted and controlled using appropriate models. One of
the conventional models to measure and control the risks arising from fluctuations in the capital market is using the concept of Value at Risk (VaR). Which is introduced as a standardized measuring risk tool not only for those financial institutions that are large-scale commercial operations, but also for small banks, insurance companies, investment institutions and non-financial businesses. Since Value at Risk is analogous to the methods, different assets and businesses, in recent years Value at Risk becomes prevalent as a new approach for measuring the risk among the managers and commercial investors. In the current financial world abrupt and unexpected changes, even a little, has strong effective in predicting future fluctuations so it can not be ignored. As a result, robust model should used to predict and control the fluctuations that enhance the power and performance estimation and prediction models. According to the importance of the issue in this paper, the robust Cipra method with an optimal smoothing parameter is used to estimate Value at Risk (VaR) for normal statistical distributions and t-student. The data used daily logarithmic
returns of the automobile industry index from Mach 2011 to September in2015.In order to validate the model, the proposed model has been compared with conventional measuring VaR methods consisting of simple moving average, exponential moving weighted average, and GARCH by using first and second backtesting of Lopez loss function, and Blanco-Ihle backtesting. The results shows that performance of proposed method in normal distribution with confidence levels of 95%, 97.5%, and 99% and also in t-student distribution with confidence levels of 97.5%, 99% is better than others.