IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2409.08355.html
   My bibliography  Save this paper

On the macroeconomic fundamentals of long-term volatilities and dynamic correlations in COMEX copper futures

Author

Listed:
  • Zian Wang
  • Xinshu Li

Abstract

This paper examines the influence of low-frequency macroeconomic variables on the high-frequency returns of copper futures and the long-term correlation with the S&P 500 index, employing GARCH-MIDAS and DCC-MIDAS modeling frameworks. The estimated results of GARCH-MIDAS show that realized volatility (RV), level of interest rates (IR), industrial production (IP) and producer price index (PPI), volatility of Slope, PPI, consumer sentiment index (CSI), and dollar index (DI) have significant impacts on Copper futures returns, among which PPI is the most efficient macroeconomic variable. From comparison among DCC-GARCH and DCC-MIDAS model, the added MIDAS filter of PPI improves the model fitness and have better performance than RV in effecting the long-run relationship between Copper futures and S&P 500.

Suggested Citation

  • Zian Wang & Xinshu Li, 2024. "On the macroeconomic fundamentals of long-term volatilities and dynamic correlations in COMEX copper futures," Papers 2409.08355, arXiv.org.
    • Handle: RePEc:arx:papers:2409.08355
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2409.08355
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ruobing Liu & Jianhui Yang & Chuan-Yang Ruan, 2019. "The Impact of Macroeconomic News on Chinese Futures," IJFS, MDPI, vol. 7(4), pages 1-14, October.
    2. Juan Ignacio Guzmán & Enrique Silva, 2018. "Copper price determination: fundamentals versus non-fundamentals," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 31(3), pages 283-300, October.
    3. Colacito, Riccardo & Engle, Robert F. & Ghysels, Eric, 2011. "A component model for dynamic correlations," Journal of Econometrics, Elsevier, vol. 164(1), pages 45-59, September.
    4. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
    5. Hammoudeh, Shawkat & Yuan, Yuan, 2008. "Metal volatility in presence of oil and interest rate shocks," Energy Economics, Elsevier, vol. 30(2), pages 606-620, March.
    6. Elder, John & Miao, Hong & Ramchander, Sanjay, 2012. "Impact of macroeconomic news on metal futures," Journal of Banking & Finance, Elsevier, vol. 36(1), pages 51-65.
    7. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2021. "Economic drivers of commodity volatility: The case of copper," Resources Policy, Elsevier, vol. 73(C).
    8. Zheng, Yan & Zhou, Min & Wen, Fenghua, 2021. "Asymmetric effects of oil shocks on carbon allowance price: Evidence from China," Energy Economics, Elsevier, vol. 97(C).
    9. Christian Conrad & Onno Kleen, 2020. "Two are better than one: Volatility forecasting using multiplicative component GARCH‐MIDAS models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(1), pages 19-45, January.
    10. Zhang, Yongmin & Wang, Ruizhi, 2022. "COVID-19 impact on commodity futures volatilities," Finance Research Letters, Elsevier, vol. 47(PA).
    11. Buncic, Daniel & Moretto, Carlo, 2015. "Forecasting copper prices with dynamic averaging and selection models," The North American Journal of Economics and Finance, Elsevier, vol. 33(C), pages 1-38.
    12. Smith, Kenneth L & Bracker, Kevin, 2003. "Forecasting Changes in Copper Futures Volatility with GARCH Models Using an Iterated Algorithm," Review of Quantitative Finance and Accounting, Springer, vol. 20(3), pages 245-265, May.
    13. Nenavath Sreenu & K.S. S. Rao & Kishan D, 2021. "The macroeconomic variables impact on commodity futures volatility: A study on Indian markets," Cogent Business & Management, Taylor & Francis Journals, vol. 8(1), pages 1939929-193, January.
    14. Robert F. Engle & Eric Ghysels & Bumjean Sohn, 2013. "Stock Market Volatility and Macroeconomic Fundamentals," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 776-797, July.
    15. Sadorsky, Perry, 2014. "Modeling volatility and correlations between emerging market stock prices and the prices of copper, oil and wheat," Energy Economics, Elsevier, vol. 43(C), pages 72-81.
    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. Zian Wang & Xinyi Lu, 2024. "COMEX Copper Futures Volatility Forecasting: Econometric Models and Deep Learning," Papers 2409.08356, arXiv.org.
    2. Nguyen, Hoang & Virbickaitė, Audronė, 2023. "Modeling stock-oil co-dependence with Dynamic Stochastic MIDAS Copula models," Energy Economics, Elsevier, vol. 124(C).
    3. Nguyen, Hoang & Javed, Farrukh, 2023. "Dynamic relationship between Stock and Bond returns: A GAS MIDAS copula approach," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 272-292.
    4. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2021. "Economic drivers of commodity volatility: The case of copper," Resources Policy, Elsevier, vol. 73(C).
    5. Dinh, Theu & Goutte, Stéphane & Nguyen, Duc Khuong & Walther, Thomas, 2022. "Economic drivers of volatility and correlation in precious metal markets," Journal of Commodity Markets, Elsevier, vol. 28(C).
    6. Stewart, Shamar L. & Isengildina Massa, Olga, 2024. "Food & Oil Price Volatility Dynamics: Insights from a TVP-SVAR-DCC-MIDAS Model," 2024 Annual Meeting, July 28-30, New Orleans, LA 343936, Agricultural and Applied Economics Association.
    7. Raza, Syed Ali & Masood, Amna & Benkraiem, Ramzi & Urom, Christian, 2023. "Forecasting the volatility of precious metals prices with global economic policy uncertainty in pre and during the COVID-19 period: Novel evidence from the GARCH-MIDAS approach," Energy Economics, Elsevier, vol. 120(C).
    8. Min Liu & Chien‐Chiang Lee & Wei‐Chong Choo, 2021. "An empirical study on the role of trading volume and data frequency in volatility forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 792-816, August.
    9. Julien Chevallier, 2020. "COVID-19 Outbreak and CO 2 Emissions: Macro-Financial Linkages," JRFM, MDPI, vol. 14(1), pages 1-18, December.
    10. Liu, Min & Lee, Chien-Chiang, 2022. "Is gold a long-run hedge, diversifier, or safe haven for oil? Empirical evidence based on DCC-MIDAS," Resources Policy, Elsevier, vol. 76(C).
    11. Guo, Jin, 2018. "Co-movement of international copper prices, China's economic activity, and stock returns: Structural breaks and volatility dynamics," Global Finance Journal, Elsevier, vol. 36(C), pages 62-77.
    12. Virbickaitė, Audronė & Nguyen, Hoang & Tran, Minh-Ngoc, 2023. "Bayesian predictive distributions of oil returns using mixed data sampling volatility models," Resources Policy, Elsevier, vol. 86(PA).
    13. Liu, Han & Yang, Peng & He, Yongda & Oxley, Les & Guo, Pengwei, 2024. "Exploring the influence of the geopolitical risks on the natural resource price volatility and correlation: Evidence from DCC-MIDAS-X model," Energy Economics, Elsevier, vol. 129(C).
    14. Alain Hecq & Marie Ternes & Ines Wilms, 2023. "Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions," Papers 2301.10592, arXiv.org, revised Nov 2024.
    15. Belcaid, Karim & El Ghini, Ahmed, 2019. "U.S., European, Chinese economic policy uncertainty and Moroccan stock market volatility," The Journal of Economic Asymmetries, Elsevier, vol. 20(C).
    16. Ma, Richie Ruchuan & Xiong, Tao, 2021. "Price explosiveness in nonferrous metal futures markets," Economic Modelling, Elsevier, vol. 94(C), pages 75-90.
    17. Huawei Niu & Tianyu Liu, 2024. "Forecasting the volatility of European Union allowance futures with macroeconomic variables using the GJR-GARCH-MIDAS model," Empirical Economics, Springer, vol. 67(1), pages 75-96, July.
    18. Díaz, Juan D. & Hansen, Erwin & Cabrera, Gabriel, 2020. "A random walk through the trees: Forecasting copper prices using decision learning methods," Resources Policy, Elsevier, vol. 69(C).
    19. repec:eee:finlet:v:24:y:2018:i:c:p:56-63 is not listed on IDEAS
    20. Ye, Wuyi & Guo, Ranran & Deschamps, Bruno & Jiang, Ying & Liu, Xiaoquan, 2021. "Macroeconomic forecasts and commodity futures volatility," Economic Modelling, Elsevier, vol. 94(C), pages 981-994.
    21. Andrea BUCCI, 2017. "Forecasting Realized Volatility A Review," Journal of Advanced Studies in Finance, ASERS Publishing, vol. 8(2), pages 94-138.

    More about this item

    Statistics

    Access and download statistics

    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:arx:papers:2409.08355. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.