An Analysis of the Interdependence Between Peanut and Other Agricultural Commodities in China’s Futures Market

ArXiv ID: 2501.16697 “View on arXiv”

Authors: Unknown

Abstract

This study analyzes historical data from five agricultural commodities in the Chinese futures market to explore the correlation, cointegration, and Granger causality between Peanut futures and related futures. Multivariate linear regression models are constructed for prices and logarithmic returns, while dynamic relationships are examined using VAR and DCC-EGARCH models. The results reveal a significant dynamic linkage between Peanut and Soybean Oil futures through DCC-EGARCH, whereas the VAR model suggests limited influence from other futures. Additionally, the application of MLP, CNN, and LSTM neural networks for price prediction highlights the critical role of time step configurations in forecasting accuracy. These findings provide valuable insights into the interconnectedness of agricultural futures markets and the efficacy of advanced modeling techniques in financial analysis.

Keywords: Agricultural Futures, DCC-EGARCH, Granger Causality, Neural Networks (MLP/CNN/LSTM), Cointegration, Commodities

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats
• Why: The paper employs advanced econometric and deep learning models (DCC-EGARCH, CNN/LSTM) but relies on historical data analysis without providing executable code, backtest performance metrics, or robustness checks needed for trading implementation.

  flowchart TD
    A["Research Goal:<br>Analyze Interdependence of<br>Agricultural Commodities in China's<br>Futures Market"] --> B["Data Input:<br>5 Commodities Futures Data<br>(Peanut, Soybean Oil, etc.)"]
    B --> C{"Methodology Steps"}
    C --> D["Statistical Analysis:<br>Correlation, Cointegration,<br>Granger Causality"]
    C --> E["Time-Series Modeling:<br>VAR, DCC-EGARCH"]
    C --> F["Machine Learning:<br>MLP, CNN, LSTM"]
    D & E & F --> G["Key Findings & Outcomes"]
    G --> H["Peanut & Soybean Oil<br>show significant dynamic linkage<br>(DCC-EGARCH)"]
    G --> I["VAR indicates limited<br>influence from other futures"]
    G --> J["Neural Networks:<br>Time step configuration critical<br>for price prediction accuracy"]