Main Article Content
Abstract
Gold is a multifunctional precious metal. Apart from being jewelry, gold is a form of investment. For this reason, the public or investors need to know the estimated daily gold price for transactions for the public or investors who want to invest or also want to sell their gold, so they do not lose. This is the aim of this study. Many forecasting methods can be used to predict the daily gold price, but this study uses the ARIMA-GARCH hybrid model because this model can predict econometric models such as the daily gold price which usually contains high volatility. Daily gold price data was secondary data obtained from the investing.com website. The data was for the period March 12, 2016, to December 31, 2020. The results of this study are obtained for the ARIMA (1,1,1) -GARCH (2,1) hybrid model with a root mean square error (RMSE) forecasting accuracy value is 2.375454, the mean absolute error (MAE) is 1.702908, and the mean absolute percentage error (MAPE) is 0.001168113. From the results of this study, long-term investment is very profitable because there is an upward trend from the model obtained. For short-term investments, the public or investors have to update the research result model because the current gold price is influenced by the gold price only one period ago, so that when trading does not lose.
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References
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- As’ad, M. (2012). Finding the Besr ARIMA model to forecast daily peak electricity demand. Proceedings of the Fifth Annual ASEARC Conference - Looking to the future - Programme and Proceedings, University of Wollongong.
- Dritsaki, S. (2018). The Performance of Hybrid ARIMA-GARCH Modeling and Forecasting Oil Price. International Journal of Energy Economics and Policy, 8(3), 14-21.
- Farida, E. & As’ad, M. (2021). The Forecasting of Monthly Inflation in Malang City Using an Autoregressive Integrated Moving Average. International Journal of Economics, Business and Accounting Research (IJEBAR), 5(2), 73-83.
- Gun, F. A., Bachtiar, F. A., & Cholissodin, I. (2019). Optimasi Fungsi Keanggotaan Fuzzy Tsukamoto dengan Algoritme Genetika pada Peramalan Harga Emas untuk Stock Trading. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 3(4), 3939-3948.
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- Idris, A.S., Mohammed, Y.A. & Imam, A. (2021). On Comparative Performances of ARIMA, hybrid ARIMA-ARCH and hybrid ARIMA-GARCH Models in Modeling The Volatility Of Foreign Exchange. Global Scientific Journal, 9(3), 31-40.
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- Khamis. A. & Yee, P.H. (2018). A Hybrid Model of Artificial Neural Network and Genetic Algorithm in Forecasting Gold Price. European Journal of Engineering Research and Science, 3(6). 10-14. https://doi.org/10.24018/ejers.2018.3.6.758
- Kurniawan, I. (2019). Analisis Keuntungan Investasi Emas dengan IHSG. Jurnal Manajemen Bisnis dan Kewirausahaan, 3(2), 16-23. DOI:10.24912/jmbk.v3i2.4955
- Lahoti, J. H. (2017). An Analytical Study on Perception of investors towards Gold as an Investment Option, PARIPEX Indian Journal of Research, 6(3), 571-572.
- Rahma, A.P., & Canggih, C. (2021). Analisis Faktor-Faktor Yang Mempengaruhi Minat Masyarakat Terhadap Investasi Emas. Jurnal Ekonomika dan Bisnis Islam, 4(2), 98-108. https://doi.org/10.26740/jekobi.v4n2.p98-108
- Tripathy, N. (2018). Forecasting Gold Price with Auto Regressive Integrated Moving Average Model. International Journal of Economics and Financial Issues, 7(4), 324-329.
- Wei, W. S. (2006). Time series Analysis: Univariate and Multivariate Methods. 2nd edition. USA: Addison Wesley.
- Yaziz, S.R., Azizan, N.A., Zakaria R. & Ahmad, M.H. (2013). The Performance of hybrid ARIMA-GARCH modeling in forecasting gold price. 20th International Congress on Modelling and Simulation, Adelaide, Australia,1201-1207.
References
Adem, K. (2017). Prediction of Gold Prices Using Artificial Neural Networks. International Journal of Engineering Research and Development Special Issue, 9(3), 83-89. DOI: 10.29137/umagd.350596
Alkaf, F. T., Putra, D., & Soebagyo. (2017). Analisis Penjualan Emas Dan Penerapan Model Estimasi Yang Efektif Pada Pt Aneka Tambang. JABE (Journal of Applied Business and Economics), 4(3), 236-249. DOI: http://dx.doi.org/10.30998/jabe.v4i3.2481
As’ad, M. (2012). Finding the Besr ARIMA model to forecast daily peak electricity demand. Proceedings of the Fifth Annual ASEARC Conference - Looking to the future - Programme and Proceedings, University of Wollongong.
Dritsaki, S. (2018). The Performance of Hybrid ARIMA-GARCH Modeling and Forecasting Oil Price. International Journal of Energy Economics and Policy, 8(3), 14-21.
Farida, E. & As’ad, M. (2021). The Forecasting of Monthly Inflation in Malang City Using an Autoregressive Integrated Moving Average. International Journal of Economics, Business and Accounting Research (IJEBAR), 5(2), 73-83.
Gun, F. A., Bachtiar, F. A., & Cholissodin, I. (2019). Optimasi Fungsi Keanggotaan Fuzzy Tsukamoto dengan Algoritme Genetika pada Peramalan Harga Emas untuk Stock Trading. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 3(4), 3939-3948.
Gunawan, A I., & Wirawati N G P. (2013). Perbandingan Berinvestasi Antara Logam Mulia Emas Dengan Saham Perusahaan Pertambangan Emas. E-Jurnal Akuntansi Universitas Udayana, 4(2), 406-420.
Idris, A.S., Mohammed, Y.A. & Imam, A. (2021). On Comparative Performances of ARIMA, hybrid ARIMA-ARCH and hybrid ARIMA-GARCH Models in Modeling The Volatility Of Foreign Exchange. Global Scientific Journal, 9(3), 31-40.
Iriani, R., & Suprayogi, N. (2018). Akuntansi Tabungan Emas Pegadaian Syariah, Jurnal Ekonomi Syariah Teori dan Terapan, 5(10), 848-859. http://dx.doi.org/10.20473/vol5iss201810pp848-859
Johari, M. (2017). Investasi emas; alternatif berinvestasi di tengah krisis global, TAFAQQUH: Jurnal Hukum Ekonomi Syariah dan Ahwal Syahsiyah, 2(2), 24-35.
Khamis. A. & Yee, P.H. (2018). A Hybrid Model of Artificial Neural Network and Genetic Algorithm in Forecasting Gold Price. European Journal of Engineering Research and Science, 3(6). 10-14. https://doi.org/10.24018/ejers.2018.3.6.758
Kurniawan, I. (2019). Analisis Keuntungan Investasi Emas dengan IHSG. Jurnal Manajemen Bisnis dan Kewirausahaan, 3(2), 16-23. DOI:10.24912/jmbk.v3i2.4955
Lahoti, J. H. (2017). An Analytical Study on Perception of investors towards Gold as an Investment Option, PARIPEX Indian Journal of Research, 6(3), 571-572.
Rahma, A.P., & Canggih, C. (2021). Analisis Faktor-Faktor Yang Mempengaruhi Minat Masyarakat Terhadap Investasi Emas. Jurnal Ekonomika dan Bisnis Islam, 4(2), 98-108. https://doi.org/10.26740/jekobi.v4n2.p98-108
Tripathy, N. (2018). Forecasting Gold Price with Auto Regressive Integrated Moving Average Model. International Journal of Economics and Financial Issues, 7(4), 324-329.
Wei, W. S. (2006). Time series Analysis: Univariate and Multivariate Methods. 2nd edition. USA: Addison Wesley.
Yaziz, S.R., Azizan, N.A., Zakaria R. & Ahmad, M.H. (2013). The Performance of hybrid ARIMA-GARCH modeling in forecasting gold price. 20th International Congress on Modelling and Simulation, Adelaide, Australia,1201-1207.