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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