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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
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