IDEAS home Printed from https://ideas.repec.org/a/spr/minecn/v38y2025i1d10.1007_s13563-024-00472-9.html
   My bibliography  Save this article

Forecasts of coking coal futures price indices through Gaussian process regressions

Author

Listed:
  • Bingzi Jin

    (Advanced Micro Devices (China) Co., Ltd.)

  • Xiaojie Xu

    (North Carolina State University)

Abstract

For both investors and decision-makers, coking coal price estimates are essential due to the commodity’s importance as a tactical energy source. The present work uses a data-set of coking coal futures price indices traded on China Dalian Commodity Exchange from January 4, 2016 to December 31, 2020 in order to investigate the applicability of Gaussian process regressions for this forecast problem. There hasn’t been enough focus on price forecasting for this important financial indicator in the literature. Bayesian optimizations and cross-validation are used in our forecast model building processes. With an out-of-sample relative root mean square error of 1.3523%, the developed models accurately forecast the price from 01/02/2020 to 12/31/2020. It is demonstrated that Gaussian process regressions are helpful for the coking coal price forecast issue. It is possible to utilize the projection’s results alone as technical forecasts or in combination with other projections for policy research that involves forming opinions about price trends.

Suggested Citation

  • Bingzi Jin & Xiaojie Xu, 2025. "Forecasts of coking coal futures price indices through Gaussian process regressions," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 38(1), pages 203-217, March.
    • Handle: RePEc:spr:minecn:v:38:y:2025:i:1:d:10.1007_s13563-024-00472-9
    DOI: 10.1007/s13563-024-00472-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13563-024-00472-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13563-024-00472-9?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. Xiaojie Xu & Yun Zhang, 2022. "Second-hand house price index forecasting with neural networks," Journal of Property Research, Taylor & Francis Journals, vol. 39(3), pages 215-236, July.
    2. Despotovic, Milan & Nedic, Vladimir & Despotovic, Danijela & Cvetanovic, Slobodan, 2016. "Evaluation of empirical models for predicting monthly mean horizontal diffuse solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 246-260.
    3. Xiaojie Xu & Yun Zhang, 2023. "Steel price index forecasting through neural networks: the composite index, long products, flat products, and rolled products," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 36(4), pages 563-582, December.
    4. Wang, Jianzhou & Dong, Yao & Wu, Jie & Mu, Ren & Jiang, He, 2011. "Coal production forecast and low carbon policies in China," Energy Policy, Elsevier, vol. 39(10), pages 5970-5979, October.
    5. Xiaojie Xu, 2018. "Causal structure among US corn futures and regional cash prices in the time and frequency domain," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(13), pages 2455-2480, October.
    6. Xiaojie Xu & Yun Zhang, 2023. "Yellow corn wholesale price forecasts via the neural network," EconomiA, Emerald Group Publishing Limited, vol. 24(1), pages 44-67, April.
    7. Xiaojie Xu, 2018. "Intraday price information flows between the CSI300 and futures market: an application of wavelet analysis," Empirical Economics, Springer, vol. 54(3), pages 1267-1295, May.
    8. Wang, Tao & Yang, Jian, 2010. "Nonlinearity and intraday efficiency tests on energy futures markets," Energy Economics, Elsevier, vol. 32(2), pages 496-503, March.
    9. Xiaojie Xu & Yun Zhang, 2022. "Contemporaneous causality among one hundred Chinese cities," Empirical Economics, Springer, vol. 63(4), pages 2315-2329, October.
    10. Xiaojie Xu, 2017. "The rolling causal structure between the Chinese stock index and futures," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 31(4), pages 491-509, November.
    11. Timmermann, Allan, 2006. "Forecast Combinations," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 4, pages 135-196, Elsevier.
    12. Xiaojie Xu, 2020. "Corn Cash Price Forecasting," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(4), pages 1297-1320, August.
    13. Xiaojie Xu, 2019. "Contemporaneous Causal Orderings of CSI300 and Futures Prices through Directed Acyclic Graphs," Economics Bulletin, AccessEcon, vol. 39(3), pages 2052-2077.
    14. Sheikholeslami, M. & Said, Zafar & Jafaryar, M., 2022. "Hydrothermal analysis for a parabolic solar unit with wavy absorber pipe and nanofluid," Renewable Energy, Elsevier, vol. 188(C), pages 922-932.
    15. Capistrán, Carlos & Timmermann, Allan, 2009. "Forecast Combination With Entry and Exit of Experts," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 428-440.
    16. Xiaojie Xu, 2017. "Short-run price forecast performance of individual and composite models for 496 corn cash markets," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(14), pages 2593-2620, October.
    17. Xu, Xiaojie & Zhang, Yun, 2023. "Network Analysis Of Housing Price Comovements Of A Hundred Chinese Cities," National Institute Economic Review, National Institute of Economic and Social Research, vol. 264, pages 110-128, May.
    18. Xiaojie Xu, 2017. "Contemporaneous causal orderings of US corn cash prices through directed acyclic graphs," Empirical Economics, Springer, vol. 52(2), pages 731-758, March.
    19. Xiaojie Xu, 2015. "Cointegration among regional corn cash prices," Economics Bulletin, AccessEcon, vol. 35(4), pages 2581-2594.
    20. Xiaojie Xu, 2018. "Cointegration and price discovery in US corn cash and futures markets," Empirical Economics, Springer, vol. 55(4), pages 1889-1923, December.
    21. Xiaojie Xu & Yun Zhang, 2023. "Neural network predictions of the high-frequency CSI300 first distant futures trading volume," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 37(2), pages 191-207, June.
    22. Cai, Haoshu & Jia, Xiaodong & Feng, Jianshe & Li, Wenzhe & Hsu, Yuan-Ming & Lee, Jay, 2020. "Gaussian Process Regression for numerical wind speed prediction enhancement," Renewable Energy, Elsevier, vol. 146(C), pages 2112-2123.
    23. Alameer, Zakaria & Fathalla, Ahmed & Li, Kenli & Ye, Haiwang & Jianhua, Zhang, 2020. "Multistep-ahead forecasting of coal prices using a hybrid deep learning model," Resources Policy, Elsevier, vol. 65(C).
    24. Zhang, Kefei & Cao, Hua & Thé, Jesse & Yu, Hesheng, 2022. "A hybrid model for multi-step coal price forecasting using decomposition technique and deep learning algorithms," Applied Energy, Elsevier, vol. 306(PA).
    25. Christoph Wegener & Christian Spreckelsen & Tobias Basse & Hans‐Jörg Mettenheim, 2016. "Forecasting Government Bond Yields with Neural Networks Considering Cointegration," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(1), pages 86-92, January.
    26. Matyjaszek, Marta & Riesgo Fernández, Pedro & Krzemień, Alicja & Wodarski, Krzysztof & Fidalgo Valverde, Gregorio, 2019. "Forecasting coking coal prices by means of ARIMA models and neural networks, considering the transgenic time series theory," Resources Policy, Elsevier, vol. 61(C), pages 283-292.
    27. Yang, Jian & Su, Xiaojing & Kolari, James W., 2008. "Do Euro exchange rates follow a martingale? Some out-of-sample evidence," Journal of Banking & Finance, Elsevier, vol. 32(5), pages 729-740, May.
    28. Xiaojie Xu & Yun Zhang, 2023. "Coking coal futures price index forecasting with the neural network," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 36(2), pages 349-359, June.
    29. Xiaojie Xu & Yun Zhang, 2023. "Cointegration between housing prices: evidence from one hundred Chinese cities," Journal of Property Research, Taylor & Francis Journals, vol. 40(1), pages 53-75, January.
    30. Xiaopeng Guo & Jiaxing Shi & Dongfang Ren, 2016. "Coal Price Forecasting and Structural Analysis in China," Discrete Dynamics in Nature and Society, Hindawi, vol. 2016, pages 1-7, October.
    31. Ding, Lili & Zhao, Zhongchao & Han, Meng, 2021. "Probability density forecasts for steam coal prices in China: The role of high-frequency factors," Energy, Elsevier, vol. 220(C).
    32. David A Bessler & Jian Yang & Metha Wongcharupan, 2003. "Price Dynamics in the International Wheat Market: Modeling with Error Correction and Directed Acyclic Graphs," Journal of Regional Science, Wiley Blackwell, vol. 43(1), pages 1-33, February.
    33. Xiaojie Xu, 2019. "Price dynamics in corn cash and futures markets: cointegration, causality, and forecasting through a rolling window approach," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 33(2), pages 155-181, June.
    34. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
    35. Jian Yang & Zheng Li & Tao Wang, 2021. "Price discovery in chinese agricultural futures markets: A comprehensive look," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(4), pages 536-555, April.
    36. Qing Zhu & Zhongyu Zhang & Rongyao Li & Kin Keung Lai & Shouyang Wang & Jian Chai, 2014. "Structural Analysis and Total Coal Demand Forecast in China," Discrete Dynamics in Nature and Society, Hindawi, vol. 2014, pages 1-10, June.
    37. Li, Miao & Xiong, Tao, 2021. "Dynamic price discovery in Chinese agricultural futures markets," Journal of Asian Economics, Elsevier, vol. 76(C).
    38. Parviz Sohrabi & Behshad Jodeiri Shokri & Hesam Dehghani, 2023. "Predicting coal price using time series methods and combination of radial basis function (RBF) neural network with time series," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 36(2), pages 207-216, June.
    39. Xiaojie Xu & Yun Zhang, 2023. "Retail Property Price Index Forecasting through Neural Networks," Journal of Real Estate Portfolio Management, Taylor & Francis Journals, vol. 29(1), pages 1-28, January.
    40. Lin Chan, Hing & Kam Lee, Shu, 1997. "Modelling and forecasting the demand for coal in China," Energy Economics, Elsevier, vol. 19(3), pages 271-287, July.
    41. Drachal, Krzysztof, 2018. "Comparison between Bayesian and information-theoretic model averaging: Fossil fuels prices example," Energy Economics, Elsevier, vol. 74(C), pages 208-251.
    42. Marta Matyjaszek & Gregorio Fidalgo Valverde & Alicja Krzemień & Krzysztof Wodarski & Pedro Riesgo Fernández, 2020. "Optimizing Predictor Variables in Artificial Neural Networks When Forecasting Raw Material Prices for Energy Production," Energies, MDPI, vol. 13(8), pages 1-15, April.
    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. Bingzi Jin & Xiaojie Xu, 2025. "Predictions of residential property price indices for China via machine learning models," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(2), pages 1481-1513, April.
    2. Xiaojie Xu & Yun Zhang, 2023. "Steel price index forecasting through neural networks: the composite index, long products, flat products, and rolled products," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 36(4), pages 563-582, December.
    3. Xiaojie Xu & Yun Zhang, 2023. "Coking coal futures price index forecasting with the neural network," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 36(2), pages 349-359, June.
    4. Bingzi Jin & Xiaojie Xu, 2025. "Machine learning price index forecasts of flat steel products," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 38(1), pages 97-117, March.
    5. Xiaojie Xu & Yun Zhang, 2022. "Forecasting the total market value of a shares traded in the Shenzhen stock exchange via the neural network," Economics Bulletin, AccessEcon, vol. 42(3), pages 1266-1279.
    6. Xiaojie Xu & Yun Zhang, 2024. "Contemporaneous causality among regional steel price indices of east, south, north, central south, northeast, southwest, and northwest China," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 37(1), pages 1-14, March.
    7. Xiaojie Xu & Yun Zhang, 2023. "Neural network predictions of the high-frequency CSI300 first distant futures trading volume," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 37(2), pages 191-207, June.
    8. Xiaojie Xu & Yun Zhang, 2022. "Commodity price forecasting via neural networks for coffee, corn, cotton, oats, soybeans, soybean oil, sugar, and wheat," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(3), pages 169-181, July.
    9. Xiaojie Xu, 2019. "Contemporaneous Causal Orderings of CSI300 and Futures Prices through Directed Acyclic Graphs," Economics Bulletin, AccessEcon, vol. 39(3), pages 2052-2077.
    10. Wu, Siping & Xia, Guilin & Liu, Lang, 2023. "A novel decomposition integration model for power coal price forecasting," Resources Policy, Elsevier, vol. 80(C).
    11. Wu, Siping & Liu, Junjie & Liu, Lang, 2024. "Interval price predictions for coal using a new multi-scale ensemble model," Energy, Elsevier, vol. 313(C).
    12. Xiaojie Xu, 2018. "Causal structure among US corn futures and regional cash prices in the time and frequency domain," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(13), pages 2455-2480, October.
    13. Ding, Lili & Zhao, Zhongchao & Han, Meng, 2021. "Probability density forecasts for steam coal prices in China: The role of high-frequency factors," Energy, Elsevier, vol. 220(C).
    14. Xu, Mengjie & Li, Xiang & Li, Qianwen & Sun, Chuanwang, 2024. "LNBi-GRU model for coal price prediction and pattern recognition analysis," Applied Energy, Elsevier, vol. 365(C).
    15. Xiaojie Xu, 2020. "Corn Cash Price Forecasting," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(4), pages 1297-1320, August.
    16. Xiaowen Wang & Ying Ma & Wen Li, 2021. "The Prediction of Gold Futures Prices at the Shanghai Futures Exchange Based on the MEEMD-CS-Elman Model," SAGE Open, , vol. 11(1), pages 21582440211, March.
    17. Shao, Qihui & Du, Yongqiang & Xue, Wenxuan & Yang, Zhiyuan & Jia, Zhenxin & Shao, Xianzhu & Xu, Xue & Duan, Hongbo & Zhu, Zhipeng, 2024. "Predicting China's thermal coal price: Does multivariate decomposition-integrated forecasting model with window rolling work?," Resources Policy, Elsevier, vol. 99(C).
    18. Paolo Libenzio Brignoli & Alessandro Varacca & Cornelis Gardebroek & Paolo Sckokai, 2024. "Machine learning to predict grains futures prices," Agricultural Economics, International Association of Agricultural Economists, vol. 55(3), pages 479-497, May.
    19. Xiaojie Xu & Yun Zhang, 2022. "Contemporaneous causality among one hundred Chinese cities," Empirical Economics, Springer, vol. 63(4), pages 2315-2329, October.
    20. Ding, Lili & Zhao, Zhongchao & Wang, Lei, 2022. "Probability density forecasts for natural gas demand in China: Do mixed-frequency dynamic factors matter?," Applied Energy, Elsevier, vol. 312(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:spr:minecn:v:38:y:2025:i:1:d:10.1007_s13563-024-00472-9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.