Paper: arXiv 2403.01745

Authors: Han-Yu Zhu, Peng-Fei Dai, Wei-Xing Zhou

Abstract

Agricultural products play a critical role in human development. With economic globalization and the financialization of agricultural products continuing to advance, the interconnections between different agricultural futures have become closer. We utilize a TVP-VAR-DY model combined with the quantile method to measure the risk spillover between 11 agricultural futures on the futures exchanges of US and China from July 9,2014, to December 31,2022. This study yielded several significant findings. Firstly, CBOT corn, soybean, and wheat were identified as the primary risk transmitters, with DCE corn and soybean as the main risk receivers. Secondly, sudden events or increased economic uncertainty can increase the overall risk spillovers. Thirdly, there is an aggregation of risk spillovers amongst agricultural futures based on the dynamic directional spillover results. Lastly, the central agricultural futures under the conditional mean are CBOT corn and soybean, while CZCE hard wheat and long-grained rice are the two risk spillover centers in extreme cases, as per the results of the spillover network and minimum spanning tree. Based on these results, decision-makers are advised to safeguard against the price risk of agricultural futures under sudden economic events, and investors can utilize the results to construct a superior investment portfolio by taking different agricultural product futures as risk-leading indicators according to various situations.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper applies a robust TVP-VAR-DY framework combined with quantile connectedness and network analytics (MST) to evaluate international risk spillovers across agricultural futures markets. While the empirical execution across normal and extreme market regimes is rigorous and comprehensive, the underlying econometric methodology relies on established academic extensions rather than introducing a novel mathematical framework.

Research Flowchart

  flowchart TD
    A[Research Goal: Analyze Sino-US Agricultural Futures Risk Spillovers] --> B[Data Inputs: 11 US & Chinese Agricultural Futures <br> July 2014 - Dec 2022]
    B --> C[Methodology: TVP-VAR-DY Model & Quantile Method]
    C --> D[Network Analysis: Dynamic Directional Spillovers & Minimum Spanning Tree]
    D --> E[Key Findings: CBOT Corn/Soybeans are Main Transmitters; DCE are Receivers]
    D --> F[Key Findings: Extreme Cases Shift Risk Centers to CZCE Wheat/Rice]
    E & F --> G[Outcomes: Portfolio Optimization & Policy Safeguards for Exogenous Shocks]

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0 of 40 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.