IDEAS home Printed from https://ideas.repec.org/b/wbk/wbpubs/41280.html
   My bibliography  Save this book

Commodity Markets Outlook, April 2024

Author

Listed:
  • World Bank

Abstract

The conflict in the Middle East has been exerting upward pressures on prices of key commodities, notably oil and gold. High commodity prices, despite relatively subdued global GDP growth, suggest some countervailing forces offsetting tepid demand, such as heightened geopolitical strains and increasing metals-intensive investments in the energy transition. Commodity prices are forecast to soften marginally in 2024 and 2025 but remain substantially above pre-pandemic levels. Unlike most other prices, crude oil prices are expected to increase in 2024, mainly reflecting geopolitical tensions. The key risk to commodity price projections relates to the possibility of a broadening of the Middle East conflict, which could lead to significantly higher oil prices, thus reigniting global inflationary pressures. Meanwhile, food insecurity worsened markedly last year, reflecting elevated food prices and armed conflicts around the world. Should such conflicts worsen, global hunger could rise substantially. Heightened uncertainty around the commodity price outlook underscores the importance of forecast accuracy. A Special Focus section evaluates the performance of five approaches used to forecast prices of three commodities—aluminum, copper, and oil. It concludes that there is no “one-approach-beats-all.” Macroeconometric models tend to be more accurate at longer horizons, mainly due to their ability to account for the impact of structural changes. It is, however, critical to incorporate judgment and information that cannot be accounted for by statistical approaches. This highlights the importance of employing a wide range of approaches when forecasting commodity prices.

Suggested Citation

  • World Bank, 2024. "Commodity Markets Outlook, April 2024," World Bank Publications - Books, The World Bank Group, number 41280.
  • Handle: RePEc:wbk:wbpubs:41280
    as

    Download full text from publisher

    File URL: https://openknowledge.worldbank.org/bitstreams/9e84a1ca-8a6b-45c1-8693-01edc068408d/download
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Jin-Liang & Zhang, Yue-Jun & Zhang, Lu, 2015. "A novel hybrid method for crude oil price forecasting," Energy Economics, Elsevier, vol. 49(C), pages 649-659.
    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. Wang, Yudong & Liu, Li & Diao, Xundi & Wu, Chongfeng, 2015. "Forecasting the real prices of crude oil under economic and statistical constraints," Energy Economics, Elsevier, vol. 51(C), pages 599-608.
    2. Kaijian He & Rui Zha & Jun Wu & Kin Keung Lai, 2016. "Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price," Sustainability, MDPI, vol. 8(4), pages 1-11, April.
    3. Yulian Zhang & Shigeyuki Hamori, 2020. "Forecasting Crude Oil Market Crashes Using Machine Learning Technologies," Energies, MDPI, vol. 13(10), pages 1-14, May.
    4. Karasu, Seçkin & Altan, Aytaç, 2022. "Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization," Energy, Elsevier, vol. 242(C).
    5. Tan, Jinghua & Li, Zhixi & Zhang, Chuanhui & Shi, Long & Jiang, Yuansheng, 2024. "A multiscale time-series decomposition learning for crude oil price forecasting," Energy Economics, Elsevier, vol. 136(C).
    6. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).
    7. Quande Qin & Huangda He & Li Li & Ling-Yun He, 2020. "A Novel Decomposition-Ensemble Based Carbon Price Forecasting Model Integrated with Local Polynomial Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1249-1273, April.
    8. Lin, Ling & Jiang, Yong & Xiao, Helu & Zhou, Zhongbao, 2020. "Crude oil price forecasting based on a novel hybrid long memory GARCH-M and wavelet analysis model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 543(C).
    9. Zuzanna Karolak, 2021. "Energy prices forecasting using nonlinear univariate models," Bank i Kredyt, Narodowy Bank Polski, vol. 52(6), pages 577-598.
    10. Zeng, Sheng & Su, Bin & Zhang, Minglong & Gao, Yuan & Liu, Jun & Luo, Song & Tao, Qingmei, 2021. "Analysis and forecast of China's energy consumption structure," Energy Policy, Elsevier, vol. 159(C).
    11. Qi Zhang & Yi Hu & Jianbin Jiao & Shouyang Wang, 2022. "Exploring the Trend of Commodity Prices: A Review and Bibliometric Analysis," Sustainability, MDPI, vol. 14(15), pages 1-22, August.
    12. Wang, Delu & Ma, Gang & Song, Xuefeng & Liu, Yun, 2017. "Energy price slump and policy response in the coal-chemical industry district: A case study of Ordos with a system dynamics model," Energy Policy, Elsevier, vol. 104(C), pages 325-339.
    13. Zhen-Yao Chen & R. J. Kuo, 2019. "Combining SOM and evolutionary computation algorithms for RBF neural network training," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1137-1154, March.
    14. Yue-Jun Zhang & Shu-Hui Li, 2019. "The impact of investor sentiment on crude oil market risks: evidence from the wavelet approach," Quantitative Finance, Taylor & Francis Journals, vol. 19(8), pages 1357-1371, August.
    15. Yuanrong Wang & Yinsen Miao & Alexander CY Wong & Nikita P Granger & Christian Michler, 2023. "Domain-adapted Learning and Interpretability: DRL for Gas Trading," Papers 2301.08359, arXiv.org, revised Sep 2023.
    16. Nicholas Apergis, 2023. "Forecasting energy prices: Quantile‐based risk models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 17-33, January.
    17. Zhang, Yue-Jun & Yao, Ting & He, Ling-Yun & Ripple, Ronald, 2019. "Volatility forecasting of crude oil market: Can the regime switching GARCH model beat the single-regime GARCH models?," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 302-317.
    18. Chen, Yan & Zhang, Lei & Zhang, Feipeng, 2024. "Forecasting crude oil volatility and stock volatility: New evidence from the quantile autoregressive model," The North American Journal of Economics and Finance, Elsevier, vol. 74(C).
    19. Han, Liyan & Lv, Qiuna & Yin, Libo, 2017. "Can investor attention predict oil prices?," Energy Economics, Elsevier, vol. 66(C), pages 547-558.
    20. Piersanti, Giovanni & Piersanti, Mirko & Cicone, Antonio & Canofari, Paolo & Di Domizio, Marco, 2020. "An inquiry into the structure and dynamics of crude oil price using the fast iterative filtering algorithm," Energy Economics, Elsevier, vol. 92(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:wbk:wbpubs:41280. 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: Tal Ayalon (email available below). General contact details of provider: https://edirc.repec.org/data/dvewbus.html .

    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.