Tariq T. Alshammari1, Mohd Tahir Ismail1, Nawaf N. Hamadneh3,*, S. Al Wadi2, Jamil J. Jaber2, Nawa Alshammari3, Mohammad H. Saleh2
Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2589-2601, 2023, DOI:10.32604/iasc.2023.024001
Abstract In this study, we proposed a new model to improve the accuracy of forecasting the stock market volatility pattern. The hypothesized model was validated empirically using a data set collected from the Saudi Arabia stock Exchange (Tadawul). The data is the daily closed price index data from August 2011 to December 2019 with 2027 observations. The proposed forecasting model combines the best maximum overlapping discrete wavelet transform (MODWT) function (Bl14) and exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model. The results show the model's ability to analyze stock market data, highlight important events that contain the most volatile data, and improve… More >
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