The Spillover Effects in the Base Metals Market with Emphasis on Technological Changes

Document Type : Research - Paper

Authors

1 M.Sc Student, Faculty of Management and Economics, University of Tarbiat Modares, Tehran, Iran

2 Assistant Professor, Faculty of Management and Economics, University of Tarbiat Modares, Tehran, Iran

3 Assistant Professor, Faculty of Finance and Accounting, College of Farabi, University of Tehran, Qom, Iran

Abstract

In the basic metals market, copper and aluminum are among the most popular metals in manufacturing and construction industries due to their uses in various industries. Given Iran's favorable rank in the production and export of copper and aluminum in the region, this study had investigated the factors affecting on their global prices in the short and long-term. Therefore, in the current research, an attempt has been made to investigate the short-term (with emphasis on the spillover effect) and long-term influencing factors on the prices of these two important metals using DECO-GARCH and NARDL models. According to the results of the DECO-GARCH model, in the short term, the price of oil, parallel metal markets such as nickel, alumina, and the dollar index with the stock index have a significant effect on the global prices of copper and aluminum. Also the existence of the spillover effect among them was confirmed. According to the results of the short-term model, the existence of the spillover effect in the models was confirmed. In fact, any fluctuation in any of the variables instantly affects the fluctuations of other variables. According to the results of the long-term model, oil price, economic growth, technological changes, industrial production index, prices of metals such as nickel and alumina, stock index, and automobile production index have a significant effect on the global prices of copper and aluminum.

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  1. Mishra, A. K., and Ghate, K. (2022). “Dynamic connectedness in non-ferrous commodity markets: evidence from India using TVP-VAR and DCC-GARCH approaches”. Resources Policy, 76: 102572.
  2. Galán-Gutiérrez, J. A., and Martín-García, R. (2021). “Cointegration between the structure of copper futures prices and Brexit”. Resources Policy, 71: 101998.
  3. Zhang, Ch., and Yu, D. (2018). “The Effect of Global Price Shocks on Chinas Precious Metals Market:A Comparative Analysis of Gold and Platinum”. Journal of Cleaner Production, 186: 652-661.
  4. Ur Rehman, M., Al-Yahyaee, Kh., Al-Jarreh, I., Mensi, W., and Vink Vo, X. (2020). “Co-movements and Spillovers between Prices of Precious Metals and non-ferrous Metals: A Multiscale Analysis”. Resources Policy, 67: 101836.
  5. عباسی، ا.، صادقی، ف.؛ 1394؛ "برآورد ارزش در معرض خطر فلزات اساسی با استفاده از رویکرد GARCH چندمتغیره". نشریه مهندسی مالی و مدیریت اوراق بهادار(مدیریت پرتفوی)، دوره 6، شماره 25، ص 62-41.
  6. آزاد، ع.، فرجی کلاریجانی، ح. ر.، روحی جویباری، ع.، احمدمحمودی، م.؛ 1395؛ "پیش‌بینی قیمت و میزان تولید فلز مس در سال 2030 میلادی و کاربرد آن برای تعیین محدوده نهایی معادن در ایران". دومین کنفرانس بین‌المللی یافته‌های نوین علوم و تکنولوژی، قم، مرکز مطالعات و تحقیقات اسلامی سروش حکمت مرتضوی.
  7. Umar, Z., Nasreen, S., Slarin, S., and Tiwari, A. (2019). “Exploring the Time and Frequency Domain Connectedness of Oil Prices and Metal Prices”. Resources Policy, 64: 101516.
  8. Mensi, W., Hammoudeh, Sh., Ur Rehman, M., Al-Maalid, A., and Kang, S. (2020). “Dynamic Risk Spillovers and Portfolio Risk Management between Precious Metal and Global Foreign Exchange Markets”. The North American of Economics and Finance, 51: 86-101.
  9. Shin, Y., Yu, B., and Greenwood-Nimmo, M. (2014). “Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework”. Festschrift in Honor of Peter Schmidt, 281-314.
  10. Schorderet, Y. (2001). “Revisiting Okun’s Law: An Hysteretic Perspective”. Mimeo:  University of California San Diego.
  11. Granger, C. W., and Yoon, G. (2002). “Hidden Cointegration”. University of California San Diego. Economics Working Paper, 2: 12-38.
  12. Schorderet, Y. (2003). “Asymmetric Cointegration”. Université de Genève/Faculté des sciences économiques et sociales.
  13. وبسایت رسمی بانک جهانی، https://worldbank.org.
  14. Buncic, D., and Moretto, C. (2015). “Forcasting Copper Prices with Dynamic Averaging and Election Models”. The North American Journal of Economics and Finance, 33: 1-33.
  15. Hu, Y., and Wen., L. (2020). “A Hybrid Deep Learning Approach by Integration LSTM-ANN Networks with GARCH Model for Copper Price Volatility Prediction”. Physica A: Statistical Mechanics and its Applications, 557: 124907. DOI: https://doi.org/10.1016/j.physa.2020.124907.
  16. Silva, E., and Guzman., J. (2017). “Copper Price Determination: Fundamentals Versus Non-Fundamentals”. Mineral Economics, 31: 283-300
  17. Romer, D. (2012). “Advanced Macroeconomics”. 4th Edition, New York: McGraw-Hill, pp. 716.
  18. Alogoskoufis, G. (2019). “Dynamic macroeconomics”. MIT Press.