Peramalan Return Saham Subsektor Perbankan Menggunakan Model ARIMA-GARCH
DOI:
https://doi.org/10.14421/fourier.2024.131.1-19Keywords:
ARIMA, GARCH, ARIMA-GARCH, MLE, Return Harga SahamAbstract
Subsektor perbankan berperan penting dalam meningkatkan iklim investasi dan pertumbuhan pasar modal di Indonesia melalui penerbitan dan penjualan saham, yang turut berkontribusi dalam pertumbuhan ekonomi negara. Peramalan return harga saham berfungsi untuk meminimalisir kerugian yang diakibatkan oleh fluktuasi. Namun, fluktuasi ini dapat menyebabkan terjadinya heteroskedastisitas yang tidak dapat ditangani oleh pemodelan time series biasa, seperti Autoregressive Integrated Moving Average (ARIMA) sehingga membutuhkan model Generalized Autoregressive Conditional Heteroskedasticity (GARCH) untuk menangani volatilitas terkait heteroskedastisitas. Oleh karena itu, tujuan penelitian ini adalah mengkaji model gabungan ARIMA dan GARCH berupa ARIMA-GARCH dan menaksir parameter menggunakan metode Maximum Likelihood Estimation (MLE). Model ARIMA-GARCH diterapkan pada data harga penutupan saham harian Bank Rakyat Indonesia (Persero) Tbk (BBRI) pada periode 1 Februari 2019 hingga 2 Januari 2024. Hasil penelitian menunjukkan bahwa model terbaik dalam peramalan return harga saham adalah model ARIMA (2,0,2)-GARCH (1,1) dan menghasilkan nilai Root Mean Square Error (RMSE) sebesar 0,01628. Kemudian, hasil peramalan menunjukkan bahwa volatilitas meningkat dari periode pertama hingga periode ke enam.
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References
Bursa Efek Indonesia, “Panduan IDX Industrial Classification Versi 1.1,” 2021.
S. Wisudani, “Analisis Pengaruh Faktor Internal dan Eksternal Terhadap Harga Saham Pada Perusahaan Manufaktor yang Terdaftar di Bursa Efek Indonesia,” Jurnal Ilmu dan Riset Akuntansi, vol. 10, no. 1, pp. 1–22, 2021.
G. Liu and C. Zhang, “Economic Policy Uncertainty and Firms’ Investment and Financing Decisions in China,” China Economic Review, vol. 63, p. 101279, 2020.
A. Pagliaro, “Forecasting Significant Stock Market Price Changes Using Machine Learning: Extra Trees Classifier Leads,” Electronics (Basel), vol. 12, no. 21, 2023.
J. Kastari, S. Martha, and N. Imro’ah Intisari, “Estimasi Indeks Harga Saham Gabungan dengan Model Generalized Autoregressive Conditional Heteroskedasticity Berdistribusi Student-T,” Buletin Ilmiah Mat. Stat. dan Terapannya (Bimaster), vol. 12, no. 2, pp. 195–204, 2023.
Y. Xiang, “Using ARIMA-GARCH Model to Analyze Fluctuation Law of International Oil Price,” Math Probl Eng, vol. 2022, pp. 1–7, 2022.
M. L. Challa, V. Malepati, and S. N. R. Kolusu, “S&P BSE Sensex and S&P BSE IT Return Forecasting Using ARIMA,” Financial Innovation, vol. 6, no. 1, pp. 1–19, 2020.
A. Kolte, H. Mal, A. Pawar, T. Bhosale, and J. K. Roy, “Volatility Analysis of BSE BANKEX companies in Indian Banking Sector using GARCH Model.,” Finance India, vol. 34, no. 2, pp. 631–640, 2020.
Y. Hu, Z. Tao, D. Xing, Z. Pan, J. Zhao, and X. Chen, “Research on Stock Returns Forecast of the Four Major Banks Based on ARMA and GARCH Model,” J Phys Conf Ser, vol. 1616, no. 2, pp. 1–6, 2020.
I. Jayanegara, H. Barry, and R. Hadikusuma, “Analisis Volatilitas pada Return Saham Properti dan Real Estate dengan Menggunakan Model ARCH-GARCH di Masa Pandemi COVID-19,” Ekonomi dan Bisnis. Politeknik Negeri Jakarta, pp. 1–6, 2021.
S. Idris and Y. A. Mohammed, “On Comparative Performances of ARIMA Hybrid, ARIMA-ARCH, and Hybrid ARIMA-GARCH Models in Modeling The Volatility of Foreign Exchange,” Global Scientific Journals, vol. 9, no. 3, pp. 31–40, 2021.
J. Hartono, Portofolio dan Analisis Investasi: Pendekatan Modul, 2nd edn. Yogyakarta: Andi, 2022.
Bursa Efek Indonesia, “Panduan IDX Industrial Classification Versi 1.1,” 2021.
S. Wisudani, “Analisis Pengaruh Faktor Internal dan Eksternal Terhadap Harga Saham Pada Perusahaan Manufaktor yang Terdaftar di Bursa Efek Indonesia,” Jurnal Ilmu dan Riset Akuntansi, vol. 10, no. 1, pp. 1–22, 2021.
G. Liu and C. Zhang, “Economic Policy Uncertainty and Firms’ Investment and Financing Decisions in China,” China Economic Review, vol. 63, p. 101279, 2020.
A. Pagliaro, “Forecasting Significant Stock Market Price Changes Using Machine Learning: Extra Trees Classifier Leads,” Electronics (Basel), vol. 12, no. 21, 2023.
J. Kastari, S. Martha, and N. Imro’ah Intisari, “Estimasi Indeks Harga Saham Gabungan dengan Model Generalized Autoregressive Conditional Heteroskedasticity Berdistribusi Student-T,” Buletin Ilmiah Mat. Stat. dan Terapannya (Bimaster), vol. 12, no. 2, pp. 195–204, 2023.
Y. Xiang, “Using ARIMA-GARCH Model to Analyze Fluctuation Law of International Oil Price,” Math Probl Eng, vol. 2022, pp. 1–7, 2022.
M. L. Challa, V. Malepati, and S. N. R. Kolusu, “S&P BSE Sensex and S&P BSE IT Return Forecasting Using ARIMA,” Financial Innovation, vol. 6, no. 1, pp. 1–19, 2020.
A. Kolte, H. Mal, A. Pawar, T. Bhosale, and J. K. Roy, “Volatility Analysis of BSE BANKEX companies in Indian Banking Sector using GARCH Model.,” Finance India, vol. 34, no. 2, pp. 631–640, 2020.
Y. Hu, Z. Tao, D. Xing, Z. Pan, J. Zhao, and X. Chen, “Research on Stock Returns Forecast of the Four Major Banks Based on ARMA and GARCH Model,” J Phys Conf Ser, vol. 1616, no. 2, pp. 1–6, 2020.
I. Jayanegara, H. Barry, and R. Hadikusuma, “Analisis Volatilitas pada Return Saham Properti dan Real Estate dengan Menggunakan Model ARCH-GARCH di Masa Pandemi COVID-19,” Ekonomi dan Bisnis. Politeknik Negeri Jakarta, pp. 1–6, 2021.
S. Idris and Y. A. Mohammed, “On Comparative Performances of ARIMA Hybrid, ARIMA-ARCH, and Hybrid ARIMA-GARCH Models in Modeling The Volatility of Foreign Exchange,” Global Scientific Journals, vol. 9, no. 3, pp. 31–40, 2021.
J. Hartono, Portofolio dan Analisis Investasi: Pendekatan Modul, 2nd edn. Yogyakarta: Andi, 2022.
R. S. Tsay, Analysis of Financial Time Series, Edisi Pertama. Canada: John Wiley and Sons, Inc, 2002.
D. A. Dickey and W. A. Fuller, “Distribution of the Estimators for Autoregressive Time Series With a Unit Root,” J Am Stat Assoc, vol. 74, no. 366, pp. 427–431, 1979.
W. W. S. Wei, Time Series Analysis: Univariate and Multivariate Methods, 2nd edn. New York: Pearson Addison Wesley, 2006.
A. L. Schaffer, T. A. Dobbins, and S. A. Pearson, “Interrupted Time Series Analysis using Autoregressive Integrated Moving Average (ARIMA) Aodels: A Guide for Evaluating Large-Scale Health Interventions,” BMC Medical Research Methodolog, vol. 21, no. 58, pp. 1–12, 2021.
Q. Zhao, X. Liu, and J. Fang, “Extreme Gradient Boosting Model for Day-Ahead STLF in National Level Power System: Estonia Case Study,” Energies (Basel), vol. 16, no. 24, pp. 1–29, 2023.
P. J. Brockwell and R. A. Davis, Introduction to Time Series and Forecasting, 3th edn. New York: Springer, 2016.
T. Bollerslev, R. Y. Chou, and K. F. Kroner, “ARCH Modeling in Finance: A Review of the Theory and Empirical Evidence,” J Econom, vol. 52, pp. 5–59, 1992.
F. Fitriyani, S. A. Fasya, M. R. Irfan, and T. T. Ammar, “Peramalan Indeks Harga Saham PT Verena Multi Finance Tbk Dengan Metode Pemodelan ARIMA Dan ARCH-GARCH,” Jurnal Statistika, vol. 14, no. 1, pp. 11–23, 2021.
P. U. Gio and D. E. Irawan, Belajar Statistika dengan R. Medan: USU Press, 2016.
W. Enders, Applied Economic Time Series, 4th edn. New York: John Wiley and Sons, 2014.
R. J. Hyndman and A. B. Koehler, “Another Look at Measures of Forecast Accuracy,” Int J Forecast, vol. 22, no. 4, pp. 679–688, 2006.
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