Forcasting Stock Price PT. Indonesian Telecomunication with ARCH-GARCH Model
Abstract
This research discusses the modeling of time series using R software, focusing on forecasting the stock price of PT. Indonesian telecommunications with ARCH-GARCH model. The data used daily closing data on stock prices from January 6, 2020, to January 6, 2021 was obtained from the website www.finance.yahoo.com. The goal is to find out the best model arch-garch on PT. Indonesian telecommunications to find out the results of stock price forecasting the next day using the ARCH-GARCH model. The best model was ARIMA (2,1,3). The results of the ARCH-LM test showed the data contained heteroskedasticity effects or ARCH elements. The research models proposed in this study are ARCH (1) and ARCH-GARCH (1,1). The smallest AIC and BIC values of these two models are ARCH-GARCH (1,1) which is the best model for forecasting the stock price of PT. Indonesian telecommunications for the next 10 days. The study attempts to conduct stock price forecasting with the ARCH-GARCH model. The result of the forecasting of the share price of PT. Indonesian telecommunications from January 07, 2021 to January 20, 2021 respectively except for holidays is IDR 3374.884, IDR 3379.617,IDR 3378.305, IDR 3376.610, IDR 3380.050, IDR 3376.372, IDR 3379.071, IDR 3377.964, IDR 3377.515, IDR 3379.002. Forecasting results are close to factual data for forecasting the next 10 days so that they can be taken into consideration in investing by investors.
References
Arumningtyas, F., Prahutama, A., & Kartikasari, P. (2021). Value-At-Risk Analysis Using ARIMAX-GARCHX Approach For Estimating Risk Of Bank Central Asia Stock Returns. Jurnal Varian, 5(1), 71–80. https://doi.org/10.30812/varian.v5i1.1474
Azmi, U., & Syaifudin, W. H. (2020). Peramalan Harga Komoditas Dengan Menggunakan Metode Arima-Garch. Jurnal Varian, 3(2), 113–124. https://doi.org/10.30812/varian.v3i2.653
Bakhtiar, S. M., Ermawati, Syata, I., Alwi, W., Ibnas, R., & Anugrawati, S. D. (2021). Estimation of Average Value at Risk (AVaR) on Sharia Joint-Stock Index Using Glosten, Jaggnathan and Runkle (GJR) model. Proceedings of the 1st International Conference on Mathematics and Mathematics Education (ICMMEd 2020), 550(Icmmed 2020), 143–149. https://doi.org/10.2991/assehr.k.210508.056
Bilondatu, R. N., Nurman, & Isa, D. R. (2019). HARGA SAHAM PT . COWELL DEVELOPMENT Tbk . Jurnal Ilmu Matematika Dan Terapan, 13(1), 9–18. https://doi.org/https://doi.org/10.30598/barekengvol13iss1pp009-018ar676
Ermawati, E. E., Basri, N., & Nurfadilah, K. (2018). Pemodelan Beban Puncak Energi Listrik Menggunakan Model Gjr-Garch. Jurnal MSA ( Matematika Dan Statistika Serta Aplikasinya ), 6(1), 27. https://doi.org/10.24252/msa.v6i1.5123
Firdaus, M. (2020). Aplikasi Ekonometrika dengan E-Views, Stata, dan R (Elviana (ed.); cetakan 1,). PT Penerbit IPB Press.
Heriyanto. (2022, January 21). Analisa Saham. Sahamn TLKM: Perseroan Fokus Mengembangkan Layan Digital. Ajaib.Co.Id. Ajaib.co.id
Juliana, A., Hamidatun, & Muslima, R. (2019). Modern Forecating Teori dan Aplikasi (GARCH, Artifical Neural Network, Neuro-Garch) (1st ed.). Penertbit Deepublish Publisher.
Krispin, R. (2019). Hands-On Time Series Analysis with R : Perform Time Series Analysis and Forecasting Using R.
Larasati Enggar Niken & Hendikawati Putriaji, Z. (2016). Analisis Volatility Forecasting Sembilan Bahan Pokok Menggunakan Metode Garch Dengan Program R. Unnes Journal of Mathematics, 5(1), 90–99.
Prasetya, B. D., Pamungkas, F. S., & Kharisudin, I. (2020). Pemodelan dan Peramalan Data Saham dengan Analisis Time Series menggunakan Python. PRISMA, Prosiding Seminar Nasional Matematika, 3, 714–718. https://journal.unnes.ac.id/sju/index.php/prisma/ ISSN
Soeksin, & Fatanah. (2020). Peramalan Harga Saham PT. Bumi Serpong Damai Tbk. dengan Metode GARCH. JAMAN (Jurnal Aplikasi Manajemen Dan Akuntansi), 1(1), 64–71. https://jurnal.poltekbiskal.ac.id/jaman/article/view/57
Tsay, R. S. (2010). Analysis of Financial Time Series (third edit). A John Wiley and Sons, INC, Publication.
Widyantomo, R. P., Hoyyi, A., & Widiharih, T. (2018). PEMODELAN VOLATILITAS RETURN PORTOFOLIO SAHAM MENGGUNAKAN FEED FORWARD NERURAL NETWORK (Studi Kasus :PT Bumi Serpong Damai Tbk. Dan PT H.M Sampoerna Tbk.). Jurnal Gaussian, 7(2), 200–211. https://doi.org/10.14710/j.gauss.v7i2.26654
Yusup, M., Purqon, A., Matematika, F., Alam, P., Bandung, I. T., Mandiri, B., Tbk, P., Lippo, P. T., Tbk, K., Indonesia, S., & Tbk, P. (2015). Aplikasi Ekonofisika Menggunakan Metode ARCH - GARCH pada Analisis Beberapa Saham Index LQ45. 512–521.
This work is licensed under a Creative Commons Attribution 4.0 International License.
