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International Journal of Mathematics and Mathematical Sciences
Volume 2017, Article ID 1749106, 6 pages
https://doi.org/10.1155/2017/1749106
Research Article

Improving Volatility Risk Forecasting Accuracy in Industry Sector

Department of Risk Management and Insurance, Faculty of Management and Finance, The University of Jordan, Amman, Jordan

Correspondence should be addressed to S. Al Wadi; ku.oc.oohay@idawla_madas

Received 5 August 2017; Revised 6 October 2017; Accepted 10 October 2017; Published 7 November 2017

Academic Editor: Niansheng Tang

Copyright © 2017 S. Al Wadi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Recently, the volatility of financial markets has contributed a necessary part to risk management. Volatility risk is characterized as the standard deviation of the constantly compound return per day. This paper presents forecasting of volatility for the Jordanian industry sector after the crisis in 2009. ARIMA and ARIMA-Wavelet Transform (WT) have been conducted in order to select the best forecasting model in the content of industry stock market data collected from Amman Stock Exchange (ASE). As a result, the researcher found that ARIMA-WT has more accuracy than ARIMA directly. The results have been introduced using MATLAB 2010a and R programming.