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

TL;DR

This paper investigates the dynamic relationships between Peanut futures and other agricultural commodities in China's futures market using statistical and neural network models, revealing significant linkages and forecasting insights.

Contribution

It combines traditional econometric models with neural networks to analyze interdependence and improve price prediction in agricultural futures markets.

Findings

Significant linkage between Peanut and Soybean Oil futures identified.

Neural networks' forecasting accuracy depends on time step configurations.

Limited influence of other futures on Peanut prices suggested by VAR model.

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.