Correlation structure analysis of the global agricultural futures market

ArXiv ID: 2310.16849 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: Random Matrix Theory (RMT), Correlation structure, Eigenvalues, Market segmentation, Asset allocation, Commodities (Agricultural Futures)

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs Random Matrix Theory (RMT), an advanced mathematical framework, to analyze eigenvalue distributions and eigenvector structures, indicating moderate to high mathematical complexity. It uses 21 years of daily closing prices for 74 agricultural futures to construct empirical correlation matrices, calculate eigenvalues, and test statistical deviations from randomness, demonstrating significant data handling and implementation efforts for backtesting.

  flowchart TD
    A["Research Goal:<br>Analyze correlation structure of<br>global agricultural futures market"] --> B["Data Input:<br>Daily prices of global agricultural<br>futures (2000-2020)"]
    B --> C["Methodology:<br>Random Matrix Theory (RMT)<br>and Eigenvalue Decomposition"]
    C --> D{"Computational Analysis<br>of Correlation Matrix"}
    D -- "Random Matrix Predictions<br>(Marchenko-Pastur)" --> E["Key Findings & Outcomes"]
    D -- "Empirical Eigenvalues<br>& Eigenvectors" --> E
    
    subgraph E ["Outcomes & Applications"]
        F1["Market Dynamics:<br>Largest eigenvalue =<br>Collective market effect"]
        F2["Market Segmentation:<br>Other large eigenvalues &<br>eigenvectors identify futures groups"]
        F3["Pairwise Correlations:<br>Smallest deviating eigenvalues<br>reveal highest-correlated pairs"]
        F4["Practical Application:<br>Risk management &<br>Asset allocation optimization"]
    end

    E --> G["Conclusion:<br>Asymmetric distribution &<br>modular market structure confirmed"]