MODELING DAILY AMMAN STOCK EXCHANGE VOLATILITY FOR SERVICES SECTOR

Abstract

There are many  forecasting techniques that can be used to help the investment community in building their  policies in the future, which lead to an appropriate choices of the assets involved in the portfolios, managing it , and pricing these assets accurately. In this paper we are trying to afford one of these methods recognized as ARIMA model, which is used in analyzing  financial time series data. The  target of this paper is  forecasting  services sector volatility in Amman Stock Exchange . As a result investment community can rely on this type of analysis to make the future prospects of selling and buying  financial securities.  Using  historical indices data  accumulated daily from the web site of Amman Stock Exchange for period 3/1/2010-10/5/2015. Stationarity achieved at level for  services 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 models for this study for services sector is ARIMA(0,0,1), lastly, the best ARIMA model was formed and tabulated in the entire paper.  

Keywords

• Volatility,ARIMA Model,Financial Time Series,Forecasting

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