Estimating and Forecasting Composite Index in Pandemic Era Using ARIMA-GARCH Model
DOI:
https://doi.org/10.30812/varian.v7i2.2103Keywords:
ARIMA, GARCH, Jakarta Composite Index, Pandemic, VolatilityAbstract
Many industries have suffered financial losses as a result of the COVID-19 epidemic. The stock market's movement has been impacted by this circumstance. Due to the influence of some people, a large number of individuals with limited trading knowledge are attempting to participate in the stock market. Market volatility should be understandable in order to gain profit instead of having losses. Therefore, it's essential to comprehend the market of the future through analysis of the data. The purpose of this study is to use ARIMA-GARCH to predict the Indonesian stock market price during. The period covered by the dataset is January 2020–December 2022. The training data indicates that ARIMA (2,1,2) is the best model for ARIMA. The results showed that data residual fitted by ARIMA (2,1,2)-GARCH (1,2) exhibits heteroscedasticity, according to the residual analysis. The MAPE score is 2%, which is relatively small. It means that ARIMA (2,1,2)-GARCH (1,2) is good enough for forecasting the Jakarta Composite Index.
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References
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Chatfield, C., & Xing, H. (2019). The Analysis of Time Series: An Introduction with R, Seventh Edition. In The Analysis of Time Series: An Introduction with R, Seventh Edition. https://doi.org/10.1201/9781498752916
Grønneberg, S., & Hjort, N. L. (2014). The Copula Information Criteria. Scandinavian Journal of Statistics, 41(2). https://doi.org/10.1111/sjos.12042
Lin, Z. (2018). Modelling and forecasting the stock market volatility of SSE Composite Index using GARCH models. Future Generation Computer Systems, 79. https://doi.org/10.1016/j.future.2017.08.033
Mills, T. C. (2019). Applied Time Series Analysis: A Practical Guide to Modeling and Forecasting. In Applied Time Series Analysis: A Practical Guide to Modeling and Forecasting. https://doi.org/10.1016/C2016-0-03956-6
Ningsih, M. (2021). Prediksi Harga Saham Harian PT BTPN Syariah Tbk Menggunakan Model Arima dan Model Garch. Jurnal Ilmiah Ekonomi Islam, 7(03). http://dx.doi.org/10.29040/jiei.v7i3.2795
Paolella, M. S. (2019). Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH. In Wiley.
Puspitaningtyas, Z. (2017). Estimating systematic risk for the best investment decisions on manufacturing company in Indonesia. Investment Management and Financial Innovations, 14(1), 46–54. https://doi.org/10.21511/imfi.14(1).2017.05
Robbetze, N., & Swanepoel, M. J. (2022). Viability of Risk-return Trade-off within a South African Context. Journal of Accounting and Investment, 23(3), 413–430. https://doi.org/10.18196/jai.v23i3.15037
Septiana, N., Primadina Hasanah, & Annisa Rahmita Soemarsono. (2022). Analisis Volatilitas Harga Saham Sekor Minyak dan Gas di Indonesia pada Masa Pandemi Covid-19 dengan Metode ARIMA-GARCH. J Statistika, 14(2). https://doi.org/10.36456/jstat.vol14.no2.a4497
Sharma, P., & Vipul. (2016). Forecasting stock market volatility using Realized GARCH model: International evidence. Quarterly Review of Economics and Finance, 59. https://doi.org/10.1016/j.qref.2015.07.005
Syamsu, R. F. (2021). Aktivitas Pasar Modal Indonesia Di Era Pandemi. Kementerian Keuangan Republik Indonesia, 2.
Thach, N., & Huy, D. (2021). Risk Management Under Impacts of Macro Economic Factors Ina Big Seafood Ex-Import Firm -Angiang Fisheries Ex-Import Jsc. In Vietnam. Management, 25(1), 172–185. https://doi.org/10.2478/manment-2019-0065
Tsay, R. S. (2017). Analysis of financial time series. In Analysis of Financial Time Series. https://doi.org/10.1002/9780470644560
Utami, D. N. (2021, March). Setahun Covid-19 di Inndonesia, Analisis: Pasar Saham Belum Sepenuhnya Pulih.
Virginia, E., Ginting, J., & Elfaki, F. A. M. (2018). Application of garch model to forecast data and volatility of share price of energy (Study on adaro energy Tbk, LQ45). International Journal of Energy Economics and Policy, 8(3). https://www.econjournals.com/index.php/ijeep/article/view/6352/3702
Wei, W. W. S. (2019). Multivariate time series analysis and applications. In Multivariate Time Series Analysis and Applications. https://doi.org/10.1002/9781119502951
Yolanda, N. B., Nainggolan, N., & Komalig, H. A. H. (2017). Penerapan Model ARIMA-GARCH Untuk Memprediksi Harga Saham Bank BRI. Jurnal MIPA, 6(2). https://doi.org/10.35799/jm.6.2.2017.17817
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Published
2024-06-30
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How to Cite
[1]
“Estimating and Forecasting Composite Index in Pandemic Era Using ARIMA-GARCH Model”, JV, vol. 7, no. 2, pp. 115–124, Jun. 2024, doi: 10.30812/varian.v7i2.2103.
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