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

BAREKENG: Jurnal Ilmu Matematika dan Terapan · Vol. 18 No. 3 (2024) · hlm. 1705–1718

DOI
10.30598/barekengvol18iss3pp1705-1718
ISSN
2615-3017, 1978-7227
Terbit
2024-08-02
Penerbit
PATTIMURA UNIVERSITY
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

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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  • Universitas Airlangga
  • Universitas Islam Madura
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  • Organisasi berbeda
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Rekan penulisOrganisasiArtikel bersama
Bagas Shata PratamaUniversitas Airlangga3
Pressylia Aluisina Putri WidyanggaAfiliasi tidak diketahui2
Bima Sakti Putra YusufAfiliasi tidak diketahui2
Eko Fajar CahyonoUniversitas Airlangga2
Nur ChamidahUniversitas Airlangga2
Grace Lucyana KoesnadiUniversitas Airlangga2
Dzuria Hilma Qurotu AinUniversitas Airlangga2
Fatiha Nadia SalsabilaUniversitas Airlangga2
Alfi Nur NitasariUniversitas Airlangga2
Irma Ayu IndrastaUniversitas Airlangga2
Hanny ValidaUniversitas Airlangga2
Farah Fauziah PutriUniversitas Airlangga2
Doni Muhammad FauziUniversitas Airlangga2
Zalfaa Nur AmaliaUniversitas Airlangga1
Rosyida Widadina UlyaUniversitas Airlangga1
Disty Ridha HastutiUniversitas Airlangga1
Adma Novita SariAfiliasi tidak diketahui1
M. Suma Firman RomadhoniAfiliasi tidak diketahui1
Faisol FaisolUniversitas Islam Madura1
Putri UkhrowiUniversitas Islam Madura1
Ira YudistiraUniversitas Islam Madura1
Kuzairi KuzairiUniversitas Islam Madura1
Naufal Ramadhan Al Akhwal SiregarUniversitas Airlangga1
Atikah FaizaUniversitas Airlangga1
Muhammad Hilmi FibryanUniversitas Airlangga1
Ardi KurniawanUniversitas Airlangga1
Adnan Syawal Adilaha SadikinUniversitas Airlangga1
Aqil Azmi ReswaraUniversitas Airlangga1
Sa’idah Zahrotul JannahUniversitas Airlangga1
Sugha Faiz Al MaulaUniversitas Airlangga1
Nicoletta Almira Dyah SetiawanUniversitas Airlangga1
Toha SaifudinUniversitas Airlangga1
Rien RecyliaUniversitas Airlangga1
Azzah Nazhifa Wina RamadhaniAfiliasi tidak diketahui1
M. Nabil SaputraUniversitas Airlangga1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://ojs3.unpatti.ac.id/index.php/barekeng/oai, pertama di-harvest 5 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc)
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	<dc:title xml:lang="en-US">FOREIGN EXCHANGE RATE PREDICTION OF INDONESIA'S LARGEST TRADING PARTNER BASED ON VECTOR ERROR CORRECTION MODEL</dc:title>
	<dc:creator>Mardianto, M. Fariz Fadillah</dc:creator>
	<dc:creator>Farizi, Muhammad Fikry Al</dc:creator>
	<dc:creator>Permana, Made Riyo Ary</dc:creator>
	<dc:creator>Zah, Alfian Iqbal</dc:creator>
	<dc:creator>Pusporani, Elly</dc:creator>
	<dc:description xml:lang="en-US">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.</dc:description>
	<dc:publisher xml:lang="en-US">PATTIMURA UNIVERSITY</dc:publisher>
	<dc:date>2024-08-02</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/12279</dc:identifier>
	<dc:identifier>10.30598/barekengvol18iss3pp1705-1718</dc:identifier>
	<dc:source xml:lang="en-US">BAREKENG: Jurnal Ilmu Matematika dan Terapan; Vol 18 No 3 (2024): BAREKENG: Journal of Mathematics and Its Application; 1705-1718</dc:source>
	<dc:source>2615-3017</dc:source>
	<dc:source>1978-7227</dc:source>
	<dc:source>10.30598/barekengvol18iss3year2024</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/12279/8620</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 M. Fariz Fadillah Mardianto, Muhammad Fikry Al Farizi, Made Riyo Ary Permana, Alfian Iqbal Zah, Elly Pusporani</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>