FOREIGN EXCHANGE RATE PREDICTION OF INDONESIA'S LARGEST TRADING PARTNER BASED ON VECTOR ERROR CORRECTION MODEL

  • M. Fariz Fadillah Mardianto Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Indonesia https://orcid.org/0000-0002-4541-4552
  • Muhammad Fikry Al Farizi Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Indonesia
  • Made Riyo Ary Permana Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Indonesia
  • Alfian Iqbal Zah Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Indonesia
  • Elly Pusporani Department of Mathematics, Faculty of Science and Technology, Universitas Airlangga, Indonesia https://orcid.org/0000-0001-7948-3719
Keywords: Exchange Rate, Prediction, Trade, VECM, Partnerships and Economic Growth

Abstract

Foreign exchange rates from the currencies of trading partners are a critical element in the development of Indonesia's economic landscape. As an active country in international trade, Indonesia's economic health is highly dependent on trade partnerships, movements, and interactions of foreign exchange rates from Indonesia's main trading partners. To achieve economic stability, Bank Indonesia intervenes in the foreign exchange market to keep the Rupiah exchange rate within a reasonable range. Indonesia is committed to achieving several points in the Sustainable Development Goals (SDGs), such as point 17, which emphasizes partnerships, and point 8, which underlines inclusive and sustainable economic growth. This commitment is an important factor in Indonesia's economic development. Therefore, it is necessary to predict the exchange rate value of Indonesia's largest trading partners considering these SDG aspects. In this study, the Vector Error Correction Model (VECM) was used to predict the foreign exchange rate of Indonesia's largest trading partners. The data used in this study is secondary data obtained from the investing.com webpage, comprising weekly data from January 2021 to November 2023. The foreign exchange rates of Indonesia's largest trading partners have a cointegration relationship, indicating long-term relationships and similarities in movements. The best model identified is VECM (1), with a very accurate MAPE value of 3.29%. The Impulse Response Function (IRF) analysis shows that the Chinese Yuan responds variably to different currencies, stabilizing over time. Variance Decomposition reveals that short-term fluctuations in the Chinese Yuan are primarily influenced by itself (87.89%) and significantly by the Singapore Dollar, South Korean Won, and Taiwan Dollar. The Granger Causality Test indicates that the Philippine Peso influences 11 other exchange rates, refining the VECM model and improving prediction accuracy. Indonesia is expected to build economic collaborations that can help achieve economic stability.

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References

Badan Pusat Statistik Indonesia, “Berita Resmi Statistik.” 2023. Accessed: Jan. 18, 2024. [Online]. Available: https://www.bps.go.id/id/pressrelease

Badan Pusat Statistik Indonesia, “Statistik Perdagangan Luar Negeri Indonesia Ekspor, 2022, Jilid I.” Accessed: Jan. 18, 2024. [Online]. Available: https://www.bps.go.id/id/publication/2023/07/07/f6ea774181ca7b3fd0b1540e/statistik-perdagangan-luar-negeri-indonesia-ekspor-2022-jilid-i.html

W. Thorbecke, “The Weak Rupiah: Catching the Tailwinds and Avoiding the Shoals,” J. Soc. Econ. Dev., vol. 23, no. S3, pp. 521–539, Dec. 2021, doi: 10.1007/s40847-020-00111-3.

United Nations Department of Economic and Social Affairs, The Sustainable Development Goals Report 2023: Special Edition. in The Sustainable Development Goals Report. United Nations, 2023. doi: 10.18356/9789210024914.

N. B. Mokoena, “A Comparative Study of the VECM, GARCH and Multivariate GARCH Techniques in Modelling External Debt,” Thesis, North-West University (South Africa), 2021. Accessed: Jan. 18, 2024. [Online]. Available: https://repository.nwu.ac.za/handle/10394/38171

Investing, “Forex | Forex Quotes - Investing.com.” Accessed: Jan. 18, 2024. [Online]. Available: https://www.investing.com/currencies/

N. Shrestha, “Factor Analysis as a Tool for Survey Analysis,” American Journal of Applied Mathematics and Statistics, vol. 9, no. 1, pp. 4–11, 2021.

P. Buhaerah, “Pembangunan Keuangan dan Pertumbuhan Ekonomi: Studi Kasus Indonesia,” Kajian Ekonomi dan Keuangan, vol. 1, no. 2, Art. no. 2, Aug. 2017, doi: 10.31685/kek.v1i2.203.

D. N. F. Shoumi, “Analisis Kointegrasi dan Error Correction pada Hubungan Inflasi, BI Rate, dan Nilai Tukar Rupiah,” Undergraduate Thesis, Institut Teknologi Sepuluh Nopember, 2018. Accessed: Mar. 23, 2024. [Online]. Available: https://repository.its.ac.id/59069/

C. P. Ana, T. E. Lestari, and H. Permadi, “TVECM to Analyze the Relationship between Net Foreign Assets and Currency Circulation,” BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 17, no. 1, Art. no. 1, Apr. 2023, doi: 10.30598/barekengvol17iss1pp0113-0124.

U. Almahdhar, “Analisis Pengaruh Inflasi, Impor, dan Produk Domestik Bruto Terhadap Nilai Tukar Rupiah Indonesia (Pendekatan Vector Error Correction Model),” other, Universitas Jambi, 2022. Accessed: Jun. 20, 2024. [Online]. Available: https://repository.unja.ac.id/

F. A. Damayanti, “Penerapan Vector Error Correction Model pada Peramalan Nilai Tukar Rupiah terhadap Dollar Amerika,” Undergraduate Thesis, IPB University, 2020, Accessed: Mar. 23, 2024. [Online]. Available: http://repository.ipb.ac.id/handle/123456789/103736

R. R. Ahmed, J. Vveinhardt, D. Streimikiene, and M. Fayyaz, “Multivariate Granger Causality Between Macro Variables and KSE 100 Index: Evidence from Johansen Cointegration and Toda & Yamamoto Causality,” Economic Research-Ekonomska Istraživanja, vol. 30, no. 1, pp. 1497–1521, Jan. 2017, doi: 10.1080/1331677X.2017.1340176.

M. R. Hapsari, S. Astutik, and L. A. Soehono, “Relationship of Macroeconomics Variables in Indonesia Using Vector Error Correction Model,” Economics Development Analysis Journal, vol. 9, no. 4, Art. no. 4, Nov. 2020, doi: 10.15294/edaj.v9i4.38662.

X. Zou, “VECM Model Analysis of Carbon Emissions, GDP, and International Crude Oil Prices,” Discrete Dynamics in Nature and Society, vol. 2018, p. e5350308, Aug. 2018, doi: 10.1155/2018/5350308.

Y. Su, J. Cherian, M. S. Sial, A. Badulescu, P. A. Thu, D. Badulescu, and S. Samad, “Does Tourism Affect Economic Growth of China? A Panel Granger Causality Approach,” Sustainability, vol. 13, no. 3, Art. no. 3, Jan. 2021, doi: 10.3390/su13031349.

C. Hou, J. Wu, B. Cao, and J. Fan, “A Deep-Learning Prediction Model for Imbalanced Time Series Data Forecasting,” Big Data Mining and Analytics, vol. 4, no. 4, pp. 266–278, Dec. 2021, doi: 10.26599/BDMA.2021.9020011.

L. Kundu, S. Islam, Most. Z. Ferdous, M. Hossain, and P. Chakraborty, “Forecasting Economic Indicators of Bangladesh using Vector Autoregressive (VAR) Model,” Oxford Bulletin of Economics & Statistics, vol. 22, pp. 21–28, Apr. 2021.

M. U. Rehman, N. Asghar, and S. H. Kang, “Islamic Indices Provide Diversification to Bitcoin? A Time-Varying Copulas and Value at Risk Application,” Pacific-Basin Finance Journal, vol. 61, p. 101326, Jun. 2020, doi: 10.1016/j.pacfin.2020.101326.

D. V. Dinh, “Impulse Response of Inflation to Economic Growth Dynamics: VAR Model Analysis,” The Journal of Asian Finance, Economics and Business, vol. 7, no. 9, pp. 219–228, 2020.

D. Burakov and M. Freidin, “Financial Development, Economic Growth and Renewable Energy Consumption in Russia: A Vector Error Correction Approach,” International Journal of Energy Economics and Policy, vol. 7, no. 6, p. 39, 2017.

W. Jiang and Q. Yu, “Carbon Emissions and Economic Growth in China: Based on Mixed Frequency VAR Analysis,” Renewable and Sustainable Energy Reviews, vol. 183, p. 113500, Sep. 2023, doi: 10.1016/j.rser.2023.113500.

A. H. Jakada, S. Mahmood, A. U. Ahmad, I. G. Muhammad, I. A. Danmaraya, and N. S. Yahaya, “Driving Forces of CO2 Emissions Based on Impulse Response Function and Variance Decomposition: A Case of the Main African Countries,” Environmental Health Engineering And Management Journal, vol. 9, no. 3, pp. 223–232, Jul. 2022, doi: 10.34172/EHEM.2022.23.

T. A. Setyawan, A. S. Nugroho, A. Febyana, And S. Pramono, “Multiple Linear Regression Method Used to Control Nutrient Solution on Hydroponic Cultivation,” Journal of Engineering Science and Technology, vol. 17, no. 5, pp. 3460–3474, 2022.

Published
2024-08-02
How to Cite
[1]
M. F. Mardianto, M. Farizi, M. Permana, A. Zah, and E. Pusporani, “FOREIGN EXCHANGE RATE PREDICTION OF INDONESIA’S LARGEST TRADING PARTNER BASED ON VECTOR ERROR CORRECTION MODEL”, BAREKENG: J. Math. & App., vol. 18, no. 3, pp. 1705-1718, Aug. 2024.