Citation:

Regi Muzio Ponziani. "The Efficacy of BRICS Currency Forecasting Using ARIMA and Seasonal ARIMA Models" International Research Journal of Economics and Management Studies, Vol. 3, No. 7, pp. 91-101, 2024.

Abstract:

This research endeavors to forecast the currency of BRICS countries against the Indonesian Rupiah (IDR). Brazil, Russia, India, China, and South Africa comprise the BRICS economic bloc. Hence, the currencies are the Brazillian Real (BRL), Russian Ruble (RUB), India Rupee (INR), Chinese Yuan (CNY), and South African Rand (ZAR). The research period ranged from 6 April 2020 up to 21 April 2023. The data used were all post-pandemic data to mitigate the occurrence of structural break if we combine pre and post-pandemic. The results show that SARIMA(1,1,1)(1,1,1) is the best model for forecasting BRICS currencies. ARIMA(1,1,1) and ARIMA(1,1,0) also perform excellently for the forecasting purpose. This shows that the autoregressive component plays a very crucial part in the forecasting process. Models that rely solely on moving average components perform worse. The forecast results even lack variability, rendering the results unable to capture the seasonal pattern of currency movement. This research is the first research that attempts to forecast BRICS currencies using ARIMA and SARIMA models. Future research may include more than one autoregressive component to discern whether the forecasting performance is still outstanding.

References:

[1] Tampubolon, J., & Nababan, T. S. (2018). International Trade and North-Sumatra’s Local Economy. JEJAK: Journal of Economics and Policy, 11(2), 323-337. doi:https://doi.org/10.15294/jejak.v11i2.16055
[2] Sudarmawan, B. N. (2022). The Correlation of International Trade and Growth in Indonesia. Jurnal Ekonomi dan Pembangunan, 30(1), 31-46. doi:http://doi.org/10.14203/JEP.30.1.2022.31-46
[3] Purnama, P. D., & Yao, M. H. (2019). The Relationship between International Trade and Economic Growth: An Empirical Finding from ASEAN Countries. International Journal of Applied Business Research, 1(2), 112-123. doi:https://doi.org/10.35313/ijabr.v1i02.72
[4] Liang, F., Zhang, H., & Fang, Y. (2022). The Analysis of Global RMB Exchange Rate Forecasting and Risk Early Warning Using ARIMA and CNN Model. Journal of Organizational and End User Computing, 34(8), 1-25. doi:10.4018/JOEUC.300762
[5] Jacks, D. S., & Novy, D. (2019). Trade Blocs and Trade Wars During the Interwar Period. Asian Economic Policy Review, 15(1), 119-136. doi:https://doi.org/10.1111/aepr.12276
[6] Kondratov, D. I. (2021). Internationalization of the Currencies of BRICS Countries. Herald Russian Academic Science, 91(1), 37-50. doi:https://doi.org/10.1134%2FS1019331621010044
[7] Pandey, T. D. (2023). Impact of Financial Inclusion on Human Development Index: Special Reference to BRICS Countries. BRICS Journal of Economics, 4(2), 209-223. doi:https://doi.org/10.3897/brics-econ.4.e96288
[8] Joo, B. A., Shawl, S., & Makina, D. (2022). The Intercation Between FDI, Host Country Characteristics and Economic Growth? A New Panel Evidence from BRICS. Journal of Economics and Development, 24(3), 247-261. doi:https://doi.org/10.1108/JED-03-2021-0035
[9] Luna, V. M. (2016). BRICS’ Bank: Possibilities and Constraints. Economia Informa, 398, 3-22. doi:https://www.elsevier.es/es-revista-economiainforma-114-articulo-brics-bank-possibilities-constraints-S0185084916300020
[10] Diniz-Maganini, A., Rasheed, A. A., & Sheng, H. H. (2023). Price Efficiency of the Foreign Exchange Rates of BRICS Countries: A Comparative Analysis. Latin America Journal of Central Banking, 4(1). doi:https://doi.org/10.1016/j.latcb.2022.100081
[11] Iqbal, B. A. (2021). BRICS as a Driver of Global Economic Growth and Development. Global Journal of Emerging Market Economies, 14(1). doi:https://doi.org/10.1177/09749101211067096
[12] Chaudhari, D., & Trivedi, P. (2022). Efficacy of Central Bank Intervention in the Foreign Exchange Market of the BRICS Countries. BRICS Journal of Economics, 3(3), 143-172. doi:10.3897/brics-econ.3.e84676
[13] Yildirim, H., Akdag, S., & Alola, A. A. (2022). Is There a Price Bubble in the Exchange Rates of the Developing Countries? The Case of BRICS and Turkey. Journal of Economics, Finance and Administrative Science, 24(54), 247-261. doi:https://doi.org/10.1108/JEFAS-04-2021-0025
[14] Saji, T. G. (2019). Can BRICS Form a Currency Union? An Analysis Under Marko Regime-Switching Framework. Global Business Review, 20(1), 151-165. doi:10.1177/0972150917721835
[15] Chatterjee, M., & Naka, I. (2020). Twenty Years of BRICS: Political and Economic Transformations through the Lens of Land. Oxford Development Studies, 50(1), 2-13. doi:https://doi.org/10.1080/13600818.2022.2033191
[16] Putri, K. S., & Halim, S. (2020). Currency Movement Forecasting Using Time Series Analysis and Long-SHort Term Memory Analysis. Journal of Industrial Optimization, 1(2), 71-80. doi:https://doi.org/10.12928/ijio.v1i2.2490
[17] Toledo, N. I. (2021). The Autoregressive Integrated Moving Average Model in Forecasting Phillipine Peso-United States Dollar Exchange. Journal of Physics: Conference Series, 1936. doi:10.1088/1742-6596/1936/1/012002
[18] Islam, M. S., & Chowdury, T. U. (2022). Application of ARIMA Model in Forecasting Exchange Rate: Evidence from Bangladesh. Asian Journal of Management Science, 11(2), 33-40. doi:https://doi.org/10.51983/ajms-2022.11.2.3325
[19] Makatjane, K. D., Molefe, E. K., & Van Wyk, R. B. (2018). The Analysis of the 2008 US Financial Crisis: An Intervention Approach. Journal of Economics and Behavioral Studies, 10(1), 59-68. doi:https://doi.org/10.22610/jebs.v10i1(J).2089
[20] Iswari, A., Angraini, Y., & Masjkur, M. (2022). Comparison of the SARIMA Model and Intervention in Forecasting the Number of Domestic Passengers at Soekarno-Hatta International Airport. Indonesian Journal of Statistics and Its Applications, 6(1), 132-146. doi:https://doi.org/10.29244/ijsa.v6i1
[21] Al-Gounmeein, R. S., & Ismail, M. T. (2020). Forecasting the Exchange Rate of the Jordanian Dinar versus the US Dollar Using a Box-Jenkins Seasonal ARIMA Model. International Journal of Mathematics and Computer Science, 15(1), 27-40.
[22] Sirisha, U. M., Belavagi, M. C., & Attigeri, G. (2022). Profit Prediction Using ARIMA, SARIMA, and LSTM Models in Time Series Forecasting: A Comparison. IEEE Access, 10, 124715-124727. doi: 10.1109/ACCESS.2022.3224938

Keywords:

ARIMA, BRICS, SARIMA, Seasonality.