Forecasting Stock Volatility Using Wavelet-based Exponential Generalized Autoregressive Conditional Heteroscedasticity Methods

作     者：Tariq T.Alshammari Mohd Tahir Ismail Nawaf N.Hamadneh S.Al Wadi Jamil J.Jaber Nawa Alshammari Mohammad H.Saleh 

作者机构：School of Mathematical ScienceUniversiti Sains MalaysiaPenangMalaysia Department of Risk Management and InsuranceFaculty of BusinessThe University of JordanJordan Department of Basic SciencesCollege of Science and Theoretical StudiesSaudi Electronic UniversityRiyadhSaudi Arabia 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第35卷第3期

页      面：2589-2601页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

主　　题：Predictive analytics mathematical models volatility index EGARCH model 

摘      要：In this study,we proposed a new model to improve the accuracy of fore-casting the stock market volatility *** hypothesized model was validated empirically using a data set collected from the Saudi Arabia stock Exchange(Tada-wul).The data is the daily closed price index data from August 2011 to December 2019 with 2027 *** proposed forecasting model combines the best maximum overlapping discrete wavelet transform(MODWT)function(Bl14)and exponential generalized autoregressive conditional heteroscedasticity(EGARCH)*** results show the model s ability to analyze stock market data,highlight important events that contain the most volatile data,and improve forecast *** results were compared from a number of mathematical mod-els,which are the non-linear spectral model,autoregressive integrated moving aver-age(ARIMA)model and EGARCH *** performance of the forecasting model will be evaluated based on some of error functions such as Mean absolute percentage error(MAPE),Mean absolute scaled error(MASE)and Root means squared error(RMSE).