Employing A Wavelet To Predict Gold Prices Using Generalized Self-Regression Models Conditioned On Heterogeneity Of Variance
DOI:
https://doi.org/10.19139/soic-2310-5070-3499Keywords:
Time Series, Generalized Models, Wavelets, GARCH-M(P, Q) Model, TGARCH Model.Abstract
The importance of using self-regression models is conditional after smoothing the variance with the fluctuations of the daily closing price of gold globally for the period 1/1/2023 until 26/12/2024, including the GARCH-M(p,q) and TEGARCH(p, q)models, and diagnosing the models with the problem of heterogeneity of variation, estimating the parameters of the models used in the greatest possible way, examining the models using tests and statistical criteria to obtain the best models that represent real data, and then processing them using the Daubechies Wavelet and the Symlets Wavelet, and examining the suitability of forecasting models, it turned out that processing data with a wave gives better results than in real data, since a model with fewer parameters was obtained, which is the TGARCH model(1,1).Downloads
Published
2026-05-08
How to Cite
Ramzi Ahmed, S., H. Taha , H., & Hayawi, H. (2026). Employing A Wavelet To Predict Gold Prices Using Generalized Self-Regression Models Conditioned On Heterogeneity Of Variance. Statistics, Optimization & Information Computing, 16(1), 147–159. https://doi.org/10.19139/soic-2310-5070-3499
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Research Articles
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Copyright (c) 2026 Saif Ramzi Ahmed, Hutheyfa H. Taha , Heyam Hayawi
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