Russia-Ukraine conflict and the quantile return connectedness of grain futures in the BRICS and international markets

Paper: arXiv 2409.19307

Authors: Yan-Hong Yang, Ying-Hui Shao, Wei-Xing Zhou

Abstract

This study investigates quantile-based connectedness among BRICS and international grain futures around the Russia-Ukraine conflict and milestones of the Black Sea Grain Initiative. Using a dynamic quantile VAR combined with a frequency-domain decomposition, we trace spillovers across market states and horizons. Spillovers are heterogeneous across quantiles, as the time-varying total connectedness index hovers near 95% in the tails, remains well above the median, and is higher before the outbreak than after. Furthermore, grain type and regional proximity strengthen pairwise connectedness. South African grain futures are persistent net receivers, whereas Argentine grain futures, U.S. soybean, and Ukrainian wheat are key transmitters. In the frequency domain, short-term components dominate total spillovers. In portfolio applications, the minimum connectedness portfolio delivers a positive Sharpe ratio under both normal and lower tail conditions. Overall, the results inform asset allocation and risk management in grain futures markets under geopolitical instability and support policy formulation.

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 6.0/10 (capped from 8.0: transaction costs not modelled; see penalty flags)
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper provides a rigorous empirical examination of quantile- and frequency-domain connectedness in BRICS and international grain futures around geopolitical shocks. It combines theoretical QVAR frameworks with real-world portfolio optimization applications (e.g., minimum connectedness portfolios) with strong statistical robustness.

Research Flowchart

  flowchart TD
    A[Research Goal: Analyze grain futures connectedness during Russia-Ukraine conflict] --> B[Data Inputs: BRICS & International Grain Futures Prices]
    B --> C[Methodology: Dynamic Quantile VAR & Frequency Decomposition]
    C --> D[Computational Analysis: Measure Time-Varying Tail Spillovers & Horizons]
    D --> E[Key Findings: Extreme tail spillovers ~95%, short-term dominance, key transmitters/receivers]
    E --> F[Portfolio Application: Minimum Connectedness Portfolio yields positive Sharpe Ratio]