Short Term Predicting Volatility Service Jordanian Sector
Abstract
Stock market volatility have added an important section in risk scholarship and it is actual problem particularly in emerging markets. Earlier, it is measured by standard deviation of the return. Consequently, in this research the volatility data will be predicted based on ARIMA model (Autoregressive Integrated Moving Average model) of the service sector in Amman Stock Exchange (ASE) from January 2019 to December 2019. Consequently this article shows that the ARIMA model has important results in prediction. Therefore, These outcomes will be helpful for the investments.
Keywords: ARIMA model, forecasting, Service sector.
DOI: 10.7176/EJBM/11-36-14
Publication date: December 31st 2019
![Download the IISTE publication guideline!](https://www.iiste.org/sub/iiste_footer_journal.jpg)
To list your conference here. Please contact the administrator of this platform.
Paper submission email: EJBM@iiste.org
ISSN (Paper)2222-1905 ISSN (Online)2222-2839
Please add our address "contact@iiste.org" into your email contact list.
This journal follows ISO 9001 management standard and licensed under a Creative Commons Attribution 3.0 License.
Copyright © www.iiste.org