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There are many forecasting techniques that can be used in the financial markets, the importance of forecasting is to able investment community to take their decisions about the future expectations, assets allocation, portfolio management, assets pricing and other benefits. This paper presents the Box-Jenkins model as one of the forecasting techniques, which we can use, in the financial time series. The main aim of this study is to predict volatility of Amman Stock Exchange as one of the emerging markets for the insurance sector. That is adopted to give an investment community a chance to plan about their buying or selling decisions for financial securities in the future. That is achieved by finding the tentative Autoregressive Integrated Moving Average (ARIMA) models that describe the equation of the forecasting sector. The data are accumulated weekly from the web site of Amman Stock Exchange using the historical indices in the period froml/1/2005-1/4/2010. We test the stationary by using unit root test which indicates that there is a stationary at level for insurance sector, and then use a minimum mean square error, t-statistics value and p-statistics value to choose the best ARIMA models at 95% confidence interval. The resulted model for this study for insurance sector is: Zt= u+ O1Zt-1+atFrom this proposed model we can get the forecasting equation for the insurance sector. Volatility has turned out to be a subject matter of massive significance to almost anyone who is concerned in the financial markets, even as a spectator. In this paper, we present the advantage of ARIMA model in forecasting financial time series data. Amman stock exchange (Jordan) in particular insurance data was selected as a tool to show the ability of ARIMA model in forecasting financial time series, experimentally. Then, the weekly data was used to compute the values of volatilities in this study. Finally, the best ARIMA model was determined.
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