IDEAS home Printed from https://ideas.repec.org/a/eee/jrpoli/v82y2023ics030142072300226x.html
   My bibliography  Save this article

Investigating price fluctuations in copper futures: Based on EEMD and Markov-switching VAR model

Author

Listed:
  • Su, Hui
  • Zhou, Na
  • Wu, Qiaosheng
  • Bi, Zhiwei
  • Wang, Yuli

Abstract

Predictable global copper prices are crucial to the transition to a green economy. This paper examines the nonlinear characteristics between the international copper futures prices and their drivers, using combined Empirical Mode Decomposition (EEMD) and the Markov-switching VAR (MSVAR) models. We decompose the monthly international copper futures prices from January 2015 to October 2021 into its low-frequency and high-frequency components by using the EEMD method. In the next step, we employ the MSVAR to examine the nonlinear fluctuation of the international copper futures prices under different regimes. The results indicate that fluctuations of the international copper futures prices exhibit a dynamic regime switching patterns that is characterized as “expansion”, “plateau” and “contraction”. The long-term stability of the international copper futures prices is determined by factors associated with demand, especially demand for strategic metals. Both the London Metal Exchange (LME) copper stocks and the LME copper stock futures have a significant impact on the international copper futures prices in each regime. In several regimes, the global refined copper consumption has no significant effect on the international copper futures prices. The increase in copper turnover is the primary driver of the international copper futures prices during the contraction regime. Regarding the supply factor, an increase in global refined copper capacity would result in a rise in the international copper futures prices during the expansion regime. While a slump would occur during a plateau or contraction regime. The financial factor, reflected by non-commercial traders affects the international copper futures prices volatility differently under different regimes. The increase in speculation reduces the market volatility during regimes of expansion and contraction. In contrast, in plateau regime, speculation increases market volatility and activates the market. The broad dollar index has little impact on the international copper futures prices during each regime. The above conclusions indicate that we should focus on the fluctuations in the international copper futures prices that are caused by the demand for strategic metals and financial factors.

Suggested Citation

  • Su, Hui & Zhou, Na & Wu, Qiaosheng & Bi, Zhiwei & Wang, Yuli, 2023. "Investigating price fluctuations in copper futures: Based on EEMD and Markov-switching VAR model," Resources Policy, Elsevier, vol. 82(C).
    • Handle: RePEc:eee:jrpoli:v:82:y:2023:i:c:s030142072300226x
    DOI: 10.1016/j.resourpol.2023.103518
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030142072300226X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.resourpol.2023.103518?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fan, Ying & Xu, Jin-Hua, 2011. "What has driven oil prices since 2000? A structural change perspective," Energy Economics, Elsevier, vol. 33(6), pages 1082-1094.
    2. Mehmet Balcilar & Reneé van Eyden & Josine Uwilingiye & Rangan Gupta, 2017. "The Impact of Oil Price on South African GDP Growth: A Bayesian Markov Switching-VAR Analysis," African Development Review, African Development Bank, vol. 29(2), pages 319-336, June.
    3. Chen, Jinyu & Zhu, Xuehong & Zhong, Meirui, 2019. "Nonlinear effects of financial factors on fluctuations in nonferrous metals prices: A Markov-switching VAR analysis," Resources Policy, Elsevier, vol. 61(C), pages 489-500.
    4. Sun, Xiaolei & Yao, Xiaoyang & Wang, Jun, 2017. "Dynamic interaction between economic policy uncertainty and financial stress: A multi-scale correlation framework," Finance Research Letters, Elsevier, vol. 21(C), pages 214-221.
    5. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    6. Ratti, Ronald A. & Vespignani, Joaquin L., 2013. "Liquidity and crude oil prices: China's influence over 1996–2011," Economic Modelling, Elsevier, vol. 33(C), pages 517-525.
    7. Xu, Jia & Tan, Xiujie & He, Gang & Liu, Yu, 2019. "Disentangling the drivers of carbon prices in China's ETS pilots — An EEMD approach," Technological Forecasting and Social Change, Elsevier, vol. 139(C), pages 1-9.
    8. Lin Liu & Heinz Schandl & James West & Meng Jiang & Zijian Ren & Dingjiang Chen & Bing Zhu, 2022. "Copper ore material footprints and transfers embodied in domestic and international trade of provinces in China," Journal of Industrial Ecology, Yale University, vol. 26(4), pages 1423-1436, August.
    9. Junior, Peterson Owusu & Tiwari, Aviral Kumar & Padhan, Hemachandra & Alagidede, Imhotep, 2020. "Analysis of EEMD-based quantile-in-quantile approach on spot- futures prices of energy and precious metals in India," Resources Policy, Elsevier, vol. 68(C).
    10. Sanders, Dwight R. & Boris, Keith & Manfredo, Mark, 2004. "Hedgers, funds, and small speculators in the energy futures markets: an analysis of the CFTC's Commitments of Traders reports," Energy Economics, Elsevier, vol. 26(3), pages 425-445, May.
    11. Brian J. Henderson & Neil D. Pearson & Li Wang, 2015. "Editor's Choice New Evidence on the Financialization of Commodity Markets," Review of Financial Studies, Society for Financial Studies, vol. 28(5), pages 1285-1311.
    12. Anoop S Kumar & Taufeeq Ajaz, 2019. "Co-movement in crypto-currency markets: evidences from wavelet analysis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-17, December.
    13. Bangzhu Zhu & Ping Wang & Julien Chevallier & Yi‐Ming Wei & Rui Xie, 2018. "Enriching the VaR framework to EEMD with an application to the European carbon market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 23(3), pages 315-328, July.
    14. Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2008. "Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm," Energy Economics, Elsevier, vol. 30(5), pages 2623-2635, September.
    15. Ronald H. Lange, 2016. "The Monetary Transmission Mechanism and Inflation Targeting: A Regime-Switching VAR Approach for Canada," Applied Economics and Finance, Redfame publishing, vol. 3(2), pages 263-279, May.
    16. Labys, W. C. & Achouch, A. & Terraza, M., 1999. "Metal prices and the business cycle," Resources Policy, Elsevier, vol. 25(4), pages 229-238, December.
    17. Yu, Hui & Ding, Yinghui & Sun, Qingru & Gao, Xiangyun & Jia, Xiaoliang & Wang, Xinya & Guo, Sui, 2021. "Multi-scale comovement of the dynamic correlations between copper futures and spot prices," Resources Policy, Elsevier, vol. 70(C).
    18. Sun, Xiaolei & Chen, Xiuwen & Wang, Jun & Li, Jianping, 2020. "Multi-scale interactions between economic policy uncertainty and oil prices in time-frequency domains," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    19. Alex F. McCalla, 2009. "World Food Prices: Causes and Consequences," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 57(1), pages 23-34, March.
    20. Apergis, Nicholas & Chatziantoniou, Ioannis & Cooray, Arusha, 2020. "Monetary policy and commodity markets: Unconventional versus conventional impact and the role of economic uncertainty," International Review of Financial Analysis, Elsevier, vol. 71(C).
    21. Ayben Koy, 2017. "Modelling Nonlinear Dynamics of Oil Futures Market," Econometric Research in Finance, SGH Warsaw School of Economics, Collegium of Economic Analysis, vol. 2(1), pages 23-42, June.
    22. Ji, Qiang & Geng, Jiang-Bo & Tiwari, Aviral Kumar, 2018. "Information spillovers and connectedness networks in the oil and gas markets," Energy Economics, Elsevier, vol. 75(C), pages 71-84.
    23. Shao, Liuguo & Hu, Wenqin & Yang, Danhui, 2020. "The price relationship between main-byproduct metals from a multiscale nonlinear Granger causality perspective," Resources Policy, Elsevier, vol. 69(C).
    24. Michael Pedersen, 2019. "The impact of commodity price shocks in a copper-rich economy: the case of Chile," Empirical Economics, Springer, vol. 57(4), pages 1291-1318, October.
    25. Geng, Jiang-Bo & Ji, Qiang & Fan, Ying, 2017. "The relationship between regional natural gas markets and crude oil markets from a multi-scale nonlinear Granger causality perspective," Energy Economics, Elsevier, vol. 67(C), pages 98-110.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, Chang & Sun, Xiaolei & Wang, Jun & Li, Jianping & Chen, Jianming, 2021. "Multiscale information transmission between commodity markets: An EMD-Based transfer entropy network," Research in International Business and Finance, Elsevier, vol. 55(C).
    2. Chen, Jinyu & Zhu, Xuehong & Zhong, Meirui, 2019. "Nonlinear effects of financial factors on fluctuations in nonferrous metals prices: A Markov-switching VAR analysis," Resources Policy, Elsevier, vol. 61(C), pages 489-500.
    3. Jin‐Yu Chen & Xue‐Hong Zhu & Mei‐Rui Zhong, 2021. "Time‐varying effects and structural change of oil price shocks on industrial output: Evidence from China's oil industrial chain," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 3460-3472, July.
    4. Wang, Yilei & Cheng, Sheng & Cao, Yan, 2022. "How does economic policy uncertainty respond to the global oil price fluctuations? Evidence from BRICS countries," Resources Policy, Elsevier, vol. 79(C).
    5. Zhou, Ying-Zhe & Huang, Jian-Bai & Chen, Jin-Yu, 2019. "Time-varying effect of the financialization of nonferrous metals markets on China's industrial sector," Resources Policy, Elsevier, vol. 64(C).
    6. Cuilin Li & Ya-Juan Du & Qiang Ji & Jiang-bo Geng, 2019. "Multiscale Market Integration and Nonlinear Granger Causality between Natural Gas Futures and Physical Markets," Sustainability, MDPI, vol. 11(19), pages 1-23, October.
    7. Shen, Yiran & Liu, Chang & Sun, Xiaolei & Guo, Kun, 2023. "Investor sentiment and the Chinese new energy stock market: A risk–return perspective," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 395-408.
    8. Liu, Yue & Sun, Huaping & Zhang, Jijian & Taghizadeh-Hesary, Farhad, 2020. "Detection of volatility regime-switching for crude oil price modeling and forecasting," Resources Policy, Elsevier, vol. 69(C).
    9. Sun, Yuying & Han, Ai & Hong, Yongmiao & Wang, Shouyang, 2018. "Threshold autoregressive models for interval-valued time series data," Journal of Econometrics, Elsevier, vol. 206(2), pages 414-446.
    10. Alain Hecq & Elisa Voisin, 2023. "Predicting Crashes in Oil Prices During The Covid-19 Pandemic with Mixed Causal-Noncausal Models," Advances in Econometrics, in: Essays in Honor of Joon Y. Park: Econometric Methodology in Empirical Applications, volume 45, pages 209-233, Emerald Group Publishing Limited.
    11. Drachal, Krzysztof, 2016. "Forecasting spot oil price in a dynamic model averaging framework — Have the determinants changed over time?," Energy Economics, Elsevier, vol. 60(C), pages 35-46.
    12. Shao, Liuguo & Zhang, Hua, 2020. "The impact of oil price on the clean energy metal prices: A multi-scale perspective," Resources Policy, Elsevier, vol. 68(C).
    13. Emmanuel Senyo Fianu, 2022. "Analyzing and Forecasting Multi-Commodity Prices Using Variants of Mode Decomposition-Based Extreme Learning Machine Hybridization Approach," Forecasting, MDPI, vol. 4(2), pages 1-27, June.
    14. Hau, Liya & Zhu, Huiming & Yu, Yang & Yu, Dongwei, 2022. "Time-frequency coherence and quantile causality between trade policy uncertainty and rare earth prices: Evidence from China and the US," Resources Policy, Elsevier, vol. 75(C).
    15. Abdollahi, Hooman & Ebrahimi, Seyed Babak, 2020. "A new hybrid model for forecasting Brent crude oil price," Energy, Elsevier, vol. 200(C).
    16. Chen, Yanhui & Zhang, Chuan & He, Kaijian & Zheng, Aibing, 2018. "Multi-step-ahead crude oil price forecasting using a hybrid grey wave model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 98-110.
    17. Algieri, Bernardina & Leccadito, Arturo, 2019. "Price volatility and speculative activities in futures commodity markets: A combination of combinations of p-values test," Journal of Commodity Markets, Elsevier, vol. 13(C), pages 40-54.
    18. Luo, Keyu & Guo, Qiang & Li, Xiafei, 2022. "Can the return connectedness indices from grey energy to natural gas help to forecast the natural gas returns?," Energy Economics, Elsevier, vol. 109(C).
    19. Li, Xin & Ma, Jian & Wang, Shouyang & Zhang, Xun, 2015. "How does Google search affect trader positions and crude oil prices?," Economic Modelling, Elsevier, vol. 49(C), pages 162-171.
    20. Shao, Liuguo & Hu, Wenqin & Yang, Danhui, 2020. "The price relationship between main-byproduct metals from a multiscale nonlinear Granger causality perspective," Resources Policy, Elsevier, vol. 69(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jrpoli:v:82:y:2023:i:c:s030142072300226x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/30467 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.