APLIKASI MODEL ARIMA GARCH DALAM PERAMALAN DATA NILAI TUKAR RUPIAH TERHADAP DOLAR TAHUN 2017-2022
Keywords: ARIMA, Covid-19, GARCH, rupiah exchange rate, time series analysis
Abstract
The Indonesian rupiah (IDR) exchange rate is used to gauge Indonesia's economic stability. Maintaining the IDR exchange rate's stability is critical since it has a direct impact on Indonesia's national monetary situation, particularly during the Covid-19 pandemic. Forecasting the rupiah exchange rate is important to do and is one way to assess government policy. The data series to be used here are IDR exchange rate from the Yahoo Finance. It consists of 271 data taken from August 2017 to October 2022. This study aims to use the Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) modeling method using the R-studio software and predict the IDR exchange rate. The ARIMA method describes the data based on a certain time series. ARCH-Lagrange Multiplier (ARCH-LM) was applied on the residuals of the best ARIMA model to test whetoer the data is heteroscedasticity. The testing result shows that the residual of the IDR exchange rate is heteroscedasticity. Therefore, the GARCH model can be used to handle it. The results of this study are obtained for the ARIMA(2,1,3) GARCH(3,6) model as the best and describe the actual data pattern with a mean absolute percentage error (MAPE) forecasting value is 1,99%.
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References
Alfira, N., Fasa, M. I., & Suharto, S. (2021). Pengaruh Covid-19 terhadap Indeks Harga Saham Gabungan (IHSG) dan Nilai Tukar Rupiah. Al-Kharaj : Jurnal Ekonomi, Keuangan & Bisnis Syariah, 3(2), 313–323. https://doi.org/10.47467/alkharaj.v3i2.356
Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31, 307–327.
Chang, P. C., Wang, Y. W., & Liu, C. H. (2007). The development of a weighted evolving fuzzy neural network for PCB sales forecasting. Expert Systems with Applications, 32(1), 86–96. https://doi.org/10.1016/j.eswa.2005.11.021
Cryer, J. D., & Chan, K.-S. (2011). Time Series Analysis with Applications in R. In Journal of the Royal Statistical Society: Series A (Statistics in Society) (Vol. 174, Issue 2). https://doi.org/10.1111/j.1467-985x.2010.00681_4.x
Desvina, A. P., & Meijer, I. O. (2018). Penerapan Model ARCH/GARCH untuk Peramalan Nilai Tukar Petani. Jurnal Sains Matematika Dan Statistika, 4(1), 43–54.
Huang, C., & Petukhina, A. (2022). Applied time series analysis and forecasting with Python (W. K. Härdle (ed.)). Springer.
Iqbal, T. A., Sadik, K., & Sumertajaya, I. M. (2014). Pemodelan Pengukuran Luas Panen Padi Nasional Menggunakan Generalized Autoregressive Conditional Heteroscedastic Model (GARCH). Jurnal Penelitian Pertanian Tanaman Pangan, 33(1), 17. https://doi.org/10.21082/jpptp.v33n1.2014.p17-26
Jamal, A., & Bhat, M. A. (2022). COVID-19 pandemic and the exchange rate movements: evidence from six major COVID-19 hot spots. Future Business Journal, 8(1). https://doi.org/10.1186/s43093-022-00126-8
Jana, P., Rokhimi, R., & Prihatiningsih, I. R. (2019). Peramalan Kurs IDR Terhadap USD Menggunakan Double Moving Averages Dan Double Exponential Smoothing. Jurnal Derivat: Jurnal Matematika Dan Pendidikan Matematika, 2(2), 48–55. https://doi.org/10.31316/j.derivat.v2i2.132
Lestari, M. I. (2020). Signifikansi Pengaruh Sentimen Pemberlakuan PSBB Terhadap Aspek Ekonomi: Pengaruh Pada Nilai Tukar Rupiahdan Stock Return(Studi Kasus Pandemi Covid-19). Jurnal Bina Akuntansi, 7(2), 223–239. https://wiyatamandala.e-journal.id/JBA/article/view/98
Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction Time Series Analysis and Forecasting. 671.
Nuripah, S. (2022). Peramalan Nilai Tukar Rupiah Terhadap Dolar Amerika dengan Pendekatan Analisis Regresi dan ARIMA. In Repository IPB. IPB University.
Setyowibowo, S., As’ad, M., Sujito, S., & Farida, E. (2022). Forecasting of Daily Gold Price using ARIMA-GARCH Hybrid Model. Jurnal Ekonomi Pembangunan, 19(2), 257–270. https://doi.org/10.29259/jep.v19i2.13903
Shumway, R. H., & Stoffer, D. S. (2016). Time Series Analysis and its Applications. In International Journal of Forecasting (fourth, Vol. 17, Issue 2). Springer. https://doi.org/10.1016/s0169-2070(01)00083-8
Suryaputri, R. V., & Kurniawati, F. (2020). Analisis ISSI, IHSG, dan Nilai Tukar Rupiah Selama Pandemi Covid-19 Rossje. Prosiding Konferensi Nasional Ekonomi Manajemen Dan Akuntansi (KNEMA), 1177, 1–17.
Wijoyo, N. A. (2016). Peramalan Nilai Tukar Rupiah Terhadap USD dengan Menggunakan Model GARCH. Kajian Ekonomi Dan Keuangan, 20(2), 169–189. https://doi.org/10.31685/kek.v20i2.187
Woodward, W. A., Sadler, B., & Robertson, S. (2022). Time Series for Data Science: Analysis and Forecasting (1st ed.). CRC Press. https://doi.org/10.1201/9781003089070
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