Value at Risk has become a benchmark for firm-wide measures of risk. It is a single, summary, statistical measure of possible portfolio losses.
It says how the market value of an asset or of a portfolio of assets is likely to decrease over a certain time period under usual conditions. More precisely, using a probability of x percent, and a holding period of t days, an entity's Value at Risk is the loss that is expected to be exceeded with a probability of only x percent during the next t-day holding periods. Volatility is one of the main drivers of Value-at-Risk (VaR) models for market risk. Therefore, it is important to select the right volatility model to capture various properties such as the lag in time, the clustering and so forth. We have chosen to use GARCH(1,1) model to forecast the volatility. In finance, having correct specification of volatility is essential. GARCH model is known as a model of non-constant volatility. That makes its strength and this is why it is often regarded as a process that generates realistic financial datas. Indeed, the model is useful for modeling the conditional variance behavior of a random term in an econometric equation and for catching volatility clustering effect on options prices. Forecasting using GARCH(1,1) model is unanimously considered in financial firms as one of the best method to estimate the volatility. Once we obtain the forecasted volatility, then Value at Risk estimation become more precise and efficient. Our XARCH software is a sort of Graphical User Interface mainly aimed to make our work available for the largest audience. The user can choose various index, options, and forward financial instrument and the quantity required. Then, he has to define the parameters such as the term time horizon of historical rates, but also the horizon time for the Value at Risk estimation. When all has been defined, the software implements a process that generates the forecasted Value at Risk.