The forecasts allow you to predict future values of historical time series data. With the possibilities of forecasting, you can make projections of future values based on the values of the past. Using projections, organizations can prepare for the changes in the economic or competitive conditions by analyzing historical time series data to predict performance and future trends. For example, in a supply chain, if the expected demand match the actual demand, significant efficiencies can be achieved in terms of production, distribution and return. Forecasts use various predictive methods based on mathematical algorithms that model the future demand based on historical time series data that can be obtained from queries and tables containing columns of date or time. The overall objective is to choose a method to generate a time series model best fit values of the past, identifying existing data patterns and projecting the model in the future to generate the forecast. The purpose of this article is to analyze the volatility behavior of the Tunisian stock returns series index TSR in daily frequency over January 1984 to June 2010 period. We will present various non linear models for the behavior of these series through the Arch models. However, these processes are based on the assumption of non autocorrelation endogenous variable in long term. In fact, these models have autocorrelation that decay very quickly when the delay increases.

volatility, ARCH model, Tunisian stock exchange, forecasting