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Soybean Futures Price Forecasting Using Dynamic Model Averaging: Do the Predictors Change over Time?

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  • Tao Xiong
  • Zhongyi Hu

Abstract

This study uses the recently proposed dynamic model averaging (DMA) and dynamic model selection (DMS) framework to develop forecasting models of Chinese soybean futures price with eight predictors, which allows both coefficients and forecasting models to evolve over time. Specifically, covering an out-of-sample period from August 2, 2005 to May 26, 2017, experimental results show that the DMA and DMS outperform the time-varying parameter model, autoregressive model, linear regression (including all predictors), and random walk on the basis of the standard accuracy measures and Diebold-Mariano (DM) test. The best predictors for forecasting soybean futures price tend to be time-varying. Policymakers and investors should realize that there are many potential predictors whose predictive powers are strong but vary over time in Chinese soybean futures price forecasting.

Suggested Citation

  • Tao Xiong & Zhongyi Hu, 2021. "Soybean Futures Price Forecasting Using Dynamic Model Averaging: Do the Predictors Change over Time?," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 57(4), pages 1198-1214, March.
    • Handle: RePEc:mes:emfitr:v:57:y:2021:i:4:p:1198-1214
    DOI: 10.1080/1540496X.2019.1618265
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    Cited by:

    1. Arunava Bandyopadhyay & Prabina Rajib, 2023. "The asymmetric relationship between Baltic Dry Index and commodity spot prices: evidence from nonparametric causality-in-quantiles test," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 36(2), pages 217-237, June.

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