Indonesia Gold Price Forecasting Using ARIMA Model (0,1,1) - GARCH (1,0)

  • Hafivah Rosvita Sari Universitas Mulawarman
  • Sri Wahyuningsih Universitas Mulawarman
  • Meiliyani Siringoringo Universitas Mulawarman

Abstract

A frequently employed time series model is the Autoregressive Integrated Moving Average (ARIMA) model. In highly volatile data, ARIMA models sometimes produce residual variances that are heteroscedasticity. One method that can overcome the problem of residual variance heteroscedasticity is the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) method. The purpose of this study is to obtain the ARIMA-GARCH model for daily gold price data in Indonesia for the period 1 January 2022 to 31 December 2022, and to obtain daily gold price forecasting results in Indonesia. The daily gold price forecasting model obtained for Indonesia is ARIMA (0,1,1) - GARCH (1,0) with a MAPE value of 0.5745% which shows that the model is very good because the MAPE value is less than 10%. The results of Indonesia's daily gold price forecast from January 1st, 2023 to January 3rd, 2023 remain stable.

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References

Aswi, & Sukarna. (2006). Analisis Deret Waktu: Teori dan Aplikasi. Makassar: Andira Publisher.
Azmi, U., & Syaifudin, W. H. (2020). Peramalan Harga Komoditas dengan Menggunakan Metode Arima-Garch. Jurnal Varian, 3(2), 113-124.
Brooks, C. (2008). Introductory Econometrics for Finance. New York: Cambridge University Press.
Desvina, A. P., & Meijer, I. O. (2018). Penerapan Model ARCH/GARCH untuk Peramalan Nilai Tukar Petani. Jurnal Sains Matematika dan Statistika, 4(2), 43-54.
IndoGold. (2023, 10 April), Harga Emas Hari Ini. Diakses pada 10 April 2023, dari https://www.indogold.id/harga-emas-hari-ini
Kanal, F. A., Manurung, T., & Prang, J. D. (2018). Penerapan Model GARCH (Generalized Autoregressive Conditional Heteroscedasticity) Dalam Menghitung Nilai Beta Saham Indeks Perfindo25. Jurnal Ilmiah Sains, 18(2), 68-74.
Makridakis, S., Wheelwright, S. C., & McGee, V. E. (1999). Metode dan Aplikasi Peramalan, Jilid 1. Jakarta: Binarupa Aksara.
Marthasari, G. I., & Djunaidy, A. (2014). Optimasi Data Latih Menggunakan Algoritma Genetika untuk Peramalan Harga Emas Berbasis Generalized Regression Neural Network. Jurnal Sistem Informasi, 5(1), 62-69.
Salamah, M., Suhartono, & Wulandari, S. P. (2003). Buku Ajar Analisis Time Series. Surabaya: Institut Teknologi Sepuluh Nopember.
Sari, R. N., Mariani, S., & Hendikawati, P. (2016). Analisis Intervensi Fungsi Step pada Harga Saham (Studi Kasus Saham PT Fast Food Indonesia Tbk). UNNES Journal of Mathematics, 5(2), 181-189.
Syahtria, M. F., Suhadak, & Firdausi, N. (2016). Dampak Inflasi, Fluktuasi Harga Minyak dan Emas Dunia Terhadap Nilai Tukar Rupiah dan Pertumbuhan Ekonomi (Studi pada Tahun 2004-2013). Jurnal Administrasi Bisnis, 32(2), 59-68.
Tsay, R. S. (2005). Analysis of Financial Time Series. New Jersey: John Wiley & Sons.
Untari, N., Mattjik, A. A., & Saefuddin, A. (2009). Analisis Deret Waktu dengan Ragam Galat Heterogen dan Asimetrik Studi Indeks Harga Saham Gabungan (IHSG) Periode 1999-2008. Forum Statistika dan Komputasi, 14(1), 22-33.
Wei, W. W. (2006). Time Series Analysis: Univariate and Multivariate Methods (Second Edition). New York: Addison Wesley Publishing Company.
Yolanda, N. B., Nainggolan, N., & Komalig, H. A. (2017). Penerapan Model ARIMA-GARCH untuk Memprediksi Harga Saham Bank BRI. Jurnal MIPA UNSRAT, 6(2), 92-96.
Published
2024-05-31
How to Cite
SARI, Hafivah Rosvita; WAHYUNINGSIH, Sri; SIRINGORINGO, Meiliyani. Indonesia Gold Price Forecasting Using ARIMA Model (0,1,1) - GARCH (1,0). EKSPONENSIAL, [S.l.], v. 15, n. 1, p. 1-10, may 2024. ISSN 2798-3455. Available at: <https://jurnal.fmipa.unmul.ac.id/index.php/exponensial/article/view/1265>. Date accessed: 10 oct. 2024. doi: https://doi.org/10.30872/eksponensial.v15i1.1265.