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All Journal Desimal: Jurnal Matematika Al-Khwarizmi: Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam Indonesian Journal of Education and Mathematical Science Community Development Journal: Jurnal Pengabdian Masyarakat ALACRITY : Journal Of Education Journal Of Human And Education (JAHE) METAL : Jurnal Sistem Mekanik dan Termal BATIK: Jurnal Pengembangan dan Pengabdian Masyarakat Multikultural INTAN CENDEKIA: JURNAL PENGABDIAN MASYARAKAT Journal of Mathematics, Computation and Statistics (JMATHCOS)
Sarmada, Sarmada
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Journal : Journal of Mathematics, Computation and Statistics (JMATHCOS)

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Forecasting PT Pertamina Geothermal Energy TBK (PGEO) Share Prices using the Arch-Garch Model Ramadhan, Ramadhan; Yurinanda, Sherli; Sarmada, Sarmada
Journal of Mathematics, Computations and Statistics Vol. 8 No. 2 (2025): Volume 08 Nomor 02 (Oktober 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i2.9493

Abstract

This study focuses on forecasting the daily closing price of PT Pertamina Geothermal Energy Tbk (PGEO) stocks, recognizing the non-stationary and volatile nature of financial time series data. Traditional forecasting methods, such as the ARIMA (Autoregressive Integrated Moving Average) model, are often insufficient for such data because they rely on the assumption of homoscedasticity, or constant variance in the residuals. An analysis of PGEO's daily stock prices from November 2023 to July 2024 revealed significant fluctuations, indicating the presence of heteroscedasticity, where the variance of the residuals is not constant. In tackling this problem, the study utilized the ARCH (Autoregressive Conditional Heteroskedasticity) and GARCH (Generalized Autoregressive Conditional Heteroskedasticity) frameworks, purpose-built to identify and model the phenomenon of volatility clustering within financial datasets. By integrating the ARIMA model with GARCH, the study aimed to create a more robust forecasting tool. After testing various combinations, the MA(1)–GARCH(1,1) model was identified as the most suitable for predicting PGEO's stock prices. This model successfully captured the fluctuating volatility and produced a highly accurate forecast, as evidenced by a Mean Absolute Percentage Error (MAPE) of just 2.97%. A MAPE value below 10% is generally considered to represent a very high level of forecasting accuracy, confirming the effectiveness of the chosen model in providing reliable short-term predictions for stock market movements. Keywords: ARCH-GARCH, Stock price forecasting, ARIMA
Co-Authors Affan, Affan Alfan Ekajati Latief Alim, Khairul Devianto, Dodi Fajrin, Dirani Amaris Gusri, Lailal Irawan, Randi Irfan, Laode Mochamad kholijah, gusmi M. Yogi Riyantama Isjoni Mardhotillah, Bunga Multahadah, Cut Multhadah, Cut Prianto, Ramdan rarasati, niken S., Adimas Prasetyo S., M. Pramuda Nugraha Safitri, Yuliana Sherly, Issaura Sormin, Corry Tasya, Anisya Yanova, Shally Yanuar, Ferra Yurinanda, Sherli Yurinanda, Sherly Yusnidar Yusnidar Z, Gusmanely Zurweni Zurweni