  This paper adopts the random matrix theory (RMT) to analyze the correlation
structure of the global agricultural futures market from 2000 to 2020. It is
found that the distribution of correlation coefficients is asymmetric and right
skewed, and many eigenvalues of the correlation matrix deviate from the RMT
prediction. The largest eigenvalue reflects a collective market effect common
to all agricultural futures, the other largest deviating eigenvalues can be
implemented to identify futures groups, and there are modular structures based
on regional properties or agricultural commodities among the significant
participants of their corresponding eigenvectors. Except for the smallest
eigenvalue, other smallest deviating eigenvalues represent the agricultural
futures pairs with highest correlations. This paper can be of reference and
significance for using agricultural futures to manage risk and optimize asset
allocation.
