Advance Search

Aplikasi Model Autoregressive Conditional Heteroscedastic-Generalized Auto Autoregressive Conditional Heteroscedastic pada Data Return Saham Bank Syariah Indonesia

DOI: https://doi.org/10.20956/ejsa.vi.24799
Article History

Submited : December 28, 2022
Published : February 14, 2023

Keywords:
The increase of the financial sector, financial information is used in the economy to model and predict the movement of capital market stocks, so investors can easily understand investment risks. Financial sector data is in the form of time series data. Financial data  is found that does not fit the assumption of heteroscedasticity, so a model is needed that can maintain heteroscedasticity. Model Autoregressive Conditional Heteroscedasticity-Generalized Autoregressive Conditional Heteroscedastic is one of the econometric models used to model heteroscedasticity data in time series. The data in this study is BSI's daily closing price data taken from 4 January 2021 to 31 August 2022 with 406 data. Based on the selection of a time series model on Bank Syariah Indonesia (BSI), the best models are ARMA (11.0) and ARCH models (1). So that the ARMA (11.0)-ARCH (1) model can be the best model for modeling and predicting BSI stock return prices.

References

  1. E. Yuliati and D. Jayanti, "Penerapan Model GARCH untuk Menguji Efisiensi Pasar Bentuk Lemah Periode 2016-2018," SAINS : Jurnal Manajemen dan Bisnis, vol. 12, no. 2, pp. 153-174, 2020.
  2. D. Sulistiowati, M. S. Syahrul and I. Rina, "Pemodelan Harga Saham Menggunakan ARMA-GARCH," Jurnal Hasil Penelitian dan Pengkajian Ilmiah Eksakta, vol. 1, no. 1, pp. 89-93, 2022.
  3. M. Iqbal and N. W. Ningsih, "Prediski Harga Saham Harian PT BTPN Syariah Tbk Menggunakan Model Arima dan Model Garch," Jurnal Ilmiah Ekonomi Islam, vol. 7, no. 3, pp. 1573-1580, 2021.
  4. J. H. Wijaya and N. M. Nugraha, "Peramalan Kinerja Perusahaan Perbankan Tahun 2017 yang Terdaftar di Bursa Efek Indonesia dengan Metode ARCH GARCH," Bisma : Jurnal Bisnis dan Manajemen, vol. 14, no. 2, pp. 101-108, 2020.
  5. Y. Kornitasari, I. W. Safitri, I. Wanakusuma and D. I. Safitri, "Peramalan Pertumbuhan Bank Syariah Indonesia Pasca Kebijakan Merger," Jurnal Ilmiah Ekonomi, vol. 8, no. 2, pp. 1470-1478, 2022.
  6. N. Clarisaa , N. Nisrina, M. Irfan and T. A. Taqiyyudin, "Penerapan Model ATCH-GARCH dalam Prediksi Harga Saham The Walt Disney," Jurnal Sains Matematika dan Statistika, vol. 7, no. 2, pp. 108-120, 2021.
  7. Sumiyati, B. D. A. Arisandi and P. R. Wilujeng, "Metode ARCH/GARCH Untuk Memprediksi Hubungan Economic Uncertainty (Covid 19) dan Volatilitas Saham," Jurnal Bisnis dan Akuntansi, vol. 24, no. 1, pp. 117-130, 2022.
  8. L. K. Sari, N. A. Achsaru and B. Sartono, "Pemodelan Volatilitas Return Saham : Studi Kasus Pasar Saham Asia," Jurnal Ekonomi dan Pembangunan Indonesia, vol. 18, no. 1, pp. 35-52, 2017.
  9. F. A. Kanal, T. Manurung and J. D. Prang, "Penerapan Model GARCH Dalam Menghitung Nilai Beta Saham Indeks PEFINDO25," Jurnal Ilmiah Sains, vol. 18, no. 2, pp. 67-74, 2018.
  10. R. N. Bilondatu, Nurwan and D. R. Isa, "Model ARCH(1) dan GARCH(1,1) pada Peramalan Harga Saham PT. Cowell Development Tbk.," Jurnal Ilmu Matematika dan Terapan, vol. 13, no. 1, pp. 9-18, 2019.
  11. F. Salsabila, R. A. Fatharani, T. A. Taqiyyuddin and M. I. Rizki, "Aplikasi Model ARCH/GARCH dalam Prediksi Laju Inflasi Bulanan Indonesia," Jurnal Sains Matematika dan Statistika, vol. 8, no. 1, pp. 34-45, 2022.
  12. A. P. Raneo and F. Muthia, "Penerapan Model GARCH dalam Peramalan Volatilitas di Bursa Efek Indonesia," Jurnal Manajemen dan Bisnis Sriwijaya, vol. 15, no. 3, pp. 194-202, 2018.
  13. V. Ratnasari and M. Nitivijaya, "Pemodelan Inflasi di Indonesia Menggunakan Model Generalized Autoregressive Conditional Heteroscedasticity (GARCH)," Inferensi, vol. 1, no. 2, pp. 71-76, 2018.

Downloads

Download data is not yet available.
Fulltext
pdf
statcounter