Dependency of The Exchange Rate with The Volume of Indonesian Aluminum Exports Using Copula
Abstract
The downstreaming of bauxite, which is one of the raw materials for aluminum, indicates that the Indonesian government is serious about managing these mining resources. As one of the leading commodities, aluminum export activities not only affect investment but also strengthen the IDR-USD exchange rate. The increasing circulation of the rupiah has a positive impact on Indonesia in the international trade market. This study models the dependence between the IDR-USD exchange rate and Indonesia's aluminum export volume using copula. Copula doesn’t require the assumption of data normality, so it is very good for measuring the dependence of economic data that is often not normally distributed. The results of the study concluded that there is a positive correlation between the two variables, although it is not significant and is very small. This positive correlation indicates that the rupiah will strengthen along with the increasing volume of Indonesia's aluminum exports.
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[1] Kementerian ESDM RI, “Peluang Investasi Bauksit Indonesia,” Booklet ESDM Bauksit 2020. Kementerian ESDM, pp. 1–37, 2020. [Online]. Available: https://www.esdm.go.id/assets/booklet/tambang-2020/08 BOOKLET ESDM BAUKSIT 2020 OK.pdf
[2] Dirjen Mineral dan Batubara, “Booklet Promosi Investasi Mineral tahun 2023 ‘Hilirisasi Bauksit,’” Direktorat Pembinaan Program Mineral dan Batubara, Kementerian ESDM RI. Kementerian ESDM, 2023. [Online]. Available: https://www.minerba.esdm.go.id/upload/ebook/20231113143857.pdf
[3] Kemenperin, Rencana Induk Pembangunan Industri Nasional 2015 - 2035. Jakarta: Pusat Komunikasi Publik Kementerian Perindustrian, 2015. [Online]. Available: https://ilmate.kemenperin.go.id/document/1615180737-RENSTRA Logam 2020 - 2024.pdf
[4] A. Saryono and Suheri, Buletin Statistik Perdagangan Luar Negeri Ekspor Menurut HS Agustus 2023. Jakarta: Badan Pusat Statistik RI, 2023.
[5] X. Huang, “Exchange rate movements and export market dynamics : evidence from China,” Economics, vol. 11, no. 1, pp. 1–27, 2017, doi: 10.5018/economics-ejournal.ja.2017-23.
[6] J. F. Rezki et al., “Macroeconomics analysis series,” Indonesia Economic Outlook. LPEM FEB Universitas Indonesia, pp. 1–19, 2023. [Online]. Available: https://lpem.org/wp-content/uploads/2022/11/Indonesia_Economic_Outlook_2023_EN.pdf
[7] K. Ignatieva and S. Trück, “Modeling spot price dependence in Australian electricity markets with applications to risk management,” Comput. Oper. Res., vol. 66, pp. 415–433, 2016, doi: 10.1016/j.cor.2015.07.019.
[8] L. Hu, “Dependence patterns across financial markets: a mixed copula approach,” Appl. Financ. Econ., vol. 16, no. 10, pp. 717–729, 2006, doi: 10.1080/09603100500426515.
[9] R. B. Nelsen, An Introduction to Copulas, 2nd ed. New York (USA): Springer Science Business Media, 2006. [Online]. Available: https://link.springer.com/book/10.1007/0-387-28678-0
[10] M. Haugh, “An Introduction to Copulas,” Quant. Risk Manag., vol. 1, 2016.
[11] A. J. McNeil, R. Frey, and P. Embrechts, Quantitative Risk Management: Concepts, Techniques and Tools. New Jersey (USA): Princeton University Press, 2005. [Online]. Available: https://press.princeton.edu/books/hardcover/9780691166278/quantitative-risk-management
[12] M. Neuhäuser, International Encyclopedia of Statistical Science. Berlin: Springer-Verlag Berlin Heidelberg, 2011. doi: 10.1007/978-3-642-04898-2.
[13] D. Wulandari, Sutrisno, and M. B. Nirwana, “Mardia’s Skewness and Kurtosis for Assessing Normality Assumption in Multivariate Regression,” Int. J. Stat. Data Sci., vol. 1, no. 1, pp. 1–6, 2021, [Online]. Available: https://journal.uii.ac.id/ENTHUSIASTIC/article/download/18511/pdf
[14] G. M. Tinungki, “Metode pendeteksian autokorelasi murni dan autokorelasi tidak murni,” J. Mat. Stat. dan Komputasi, vol. 13, no. 1, pp. 46–54, 2016, [Online]. Available: https://journal.unhas.ac.id/index.php/jmsk/article/view/3478
[15] G. M. Ljung and G. E. P. Box, “On a measure of lack of fit in time series models,” Biometrika, vol. 65, no. 2, pp. 297–303, 2014, doi: 10.2307/2335207.
[16] S. Chatterjee and J. S. Simonoff, “Time series data and autocorrelation,” in Handbook of Regression Analysis, New Jersey (USA): John Wiley & Sons, Inc., 2020, pp. 81–109. doi: 10.1002/9781118532843.ch5.
[17] U. A. Yakubu and M. P. A. Saputra, “Time series model analysis using autocorrelation function (ACF) and partial autocorrelation function (PACF) for e-wallet transactions during a pandemic,” Int. J. Glob. Oper. Res., vol. 3, no. 3, pp. 80–85, 2022, doi: 10.47194/ijgor.v3i3.168.
DOI: https://doi.org/10.18860/cauchy.v10i2.32517
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