Forecasting Insurance Sector Volatility In Amman Stock Exchange Using ARIMA Model

Abstract

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 Co‐Movement between International and Emerging Stock Markets Using ANN and Stepwise Models: Evidence from Selected Indices

“…Few scholars study the prediction of index and volatility for ASE and ASE subsectors using different models [40,41]. Reference [20] reveals that the fuzzy neural network models are superior in predicting ASEI using trading volume as an input variable.…”