Commodity futures basis prediction based on the VMD–DBO–BiGRU model

Yuanmeng Liu, Fuguo Liu, Yufeng Shi, Yuxue Zhang () and Yijia Liu ()
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Yuanmeng Liu: Institute for Financial Studies, Shandong University, Jinan, Shandong 250100, P. R. China
Fuguo Liu: ��School of Mathematics and Data Sciences, Changji University, Changji, Xinjiang 831100, P. R. China
Yufeng Shi: Institute for Financial Studies, Shandong University, Jinan, Shandong 250100, P. R. China‡School of Mathematics, Shandong University, Jinan, Shandong 250100, P. R. China§National Center for Applied Mathematics of Shandong, Shandong University, Jinan, Shandong 250100, P. R. China
Yuxue Zhang: Institute for Financial Studies, Shandong University, Jinan, Shandong 250100, P. R. China
Yijia Liu: Institute for Financial Studies, Shandong University, Jinan, Shandong 250100, P. R. China

International Journal of Financial Engineering (IJFE), 2025, vol. 12, issue 03, 1-24

Abstract: Considering the nonlinear and nonstationary characteristics of the basis, this paper proposes an ensemble model that integrates Variational Mode Decomposition (VMD), Dung Beetle Optimization (DBO), and Bidirectional Gated Recurrent Units (BiGRUs). To avoid the limitations of a single commodity and to verify the effectiveness of the proposed model, this study selects six futures varieties and analyzes models such as BP (Backpropagation), LSTM (Long Short-Term Memory), DBO–BiGRU, and VMD–BiGRU. The results demonstrate that the VMD–DBO–BiGRU model exhibits superior performance in both one-step and multi-step forecasting. It also confirms the significant effects of VMD and DBO in enhancing the accuracy and stability of predictions. Moreover, models that account for the lag factors of the basis outperform those considering other factors, exhibiting lower errors and higher fit indices.

Keywords: Basis prediction; Variational Mode Decomposition; Dung Beetle Optimization algorithm; Bidirectional Gated Recurrent Unit (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S2424786324500178

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