Model ARIMA-GARCH Pada Peramalan Harga Saham PT. Jasa Marga (Persero)
Abstract
Abstract– PT Jasa Marga is a great reputation company, the leader in comparable businesses, has a steady income, and paying dividends consistently. This paper aims to find the best model to forecast stock price of PT Jasa Marga using ARIMA-GARCH. The data used is daily stock price of PT Jasa Marga from March 2020 to March 2021. Autoregressive Integrated Moving Average (ARIMA) is a method that can be used to forecast stock prices. However, an economical data tend to have heteroscedasticity problems, one of the methods used to overcome them is Generalized Autoregressive Conditional Heteroskedasticity (GARCH). Future stock price of PT Jasa Marga is forecasted with ARIMA-GARCH model. The data is modeled with ARIMA first, if there is heteroscedasticity, combine the model with GARCH model. The result of this study indicated that ARIMA (1, 1, 1) – GARCH (2, 2) is the best model, with MAPE 1,5647
Abstrak– PT Jasa Marga adalah perusahaan yang reputasinya baik, terdepan di perusahaan-perusahaan sejenis, stabil pendapatannya, dan pembayaran devidennya konsisten. Paper ini bertujuan untuk mencari model terbaik dalam meramalkan harga saham PT Jasa Marga menggunakan ARIMA-GARCH. Data harga saham yang diolah yaitu data sekunder dari PT Jasa Marga pada Maret 2020 hingga Maret 2021. Autoregressive Integrated Moving Average (ARIMA) sebagai metode yang dapat dimanfaatkan guna meramalkan harga saham. Akan tetapi, data tentang ekonomi cenderung memiliki masalah heteroskedastisitas, metode yang umum dipakai untuk mengatasinya adalah Generalized Autoregressive Conditional Heteroskedasticity (GARCH). Harga saham PT Jasa Marga diramalkan dengan model ARIMA-GARCH. Data terlebih dahulu dimodelkan dengan ARIMA, jika didapati adanya heteroskedastisitas, maka model tersebut dikombinasikan dengan GARCH. Penelitian ini menghasilkan ARIMA (1,1,1)-GARCH(2,2) sebagai model terbaik dengan MAPE 1,5647.
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References
T. Darmadji and Fakhruddin, Buku Pasar Modal di Indonesia. Jakarta: Salemba Empat, 2012.
Jogiyanto, Teori Portofolio dan Analisis Investasi Edisi Ketujuh, Tujuh. Yogyakarta: BPFE, 2010.
D. . Montgomery, C. L. Jennings, and M. Kulahci, Introduction to Time Series Analysis and Forecasting. New Jersey: John Wiley and Sons. Inc., 2008.
W. Enders, Applied Econometric Time Series (Fourth Edition). USA: John Wiley & Sons, Inc., 2014.
W. W. S. Wei, Time Series Analysis Univariate and Multivariate Methods Second Edition. USA: Pearson Education, Inc., 2006.
J. E. Hanke and D. Wichern, Business Forecasting. New York: Prentice Hall, 2005.
B. . Bowerman and R. T. O’Connell, Forecasting and Time Series: An Applied Approach, 3rd ed. California: Duxbury Press, 1993.
Dritsaki, C. (2018). The Performance of Hybrid ARIMA-GARCH Modeling and Forecasting Oil Price. International Journal of Energy Economics and Policy. 8(3), 14-21.
Jaipuria S and Mahapatra S. (2021). A Hybrid Forecasting Technique to Deal with Heteroskedastic Demand in a Supply Chain. Operation and Supply Chain Magaement. 14(2), 123-132.
