Investigating the influence of energy dynamics on price forecasts in tropical and traditional agricultural futures markets

ABSTRACT

This research aims to investigate the intricate dynamics between energy futures prices and both tropical and traditional commodities futures prices, with a particular focus on the critical role of oil futures prices in predicting the trajectory of agricultural futures prices. The study employs the Dynamic Conditional Correlation-Multivariate Generalized Autoregressive Conditional Heteroskedasticity (DCC-MGARCH) model to analyse the interplay among oil and agricultural futures price series over 2008:01–2021:06. Additionally, it incorporates the innovative Spatio-temporal Information Recombination Hypergraph Neural Network (STIR-HGNN) model to highlight the differences in how energy futures prices predict tropical and traditional agricultural futures prices. The findings reveal numerous connections between oil prices and both tropical and traditional agricultural futures prices, underscoring the significant role of oil prices in forecasting agricultural futures price movements. The empirical insights from this study provide valuable guidance for futures market participants, encouraging them to use these findings to refine and optimize their market strategies, thus enhancing their ability to navigate and capitalize on the complexities of these interconnected markets.

KEYWORDS:

• Oil futures markets
• agricultural futures prices forecast
• DCC-MGARCH model
• STIR-HGNN model

JEL CODES:

• G17
• F37

Additional information

Funding

The work was supported by the National Natural Science Foundation of China [Grant No. 72303221].