Contemporaneous and lagged spillovers between agriculture, crude oil, carbon emission allowance, and climate change

Paper: arXiv 2408.09669

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

Abstract

In this paper, we examine the dynamic spillovers among the crude oil, carbon emission allowance, climate change, and agricultural markets. Adopting a novel $R^2$ decomposed connectedness approach, our empirical analysis reveals several key findings. The overall TCI dynamics have been mainly dominated by contemporaneous dynamics rather than the lagged dynamics. We also find climate change has significant spillovers to other markets. Moreover, there are heterogeneous spillover effects among agricultural markets. Specially, corn is the biggest risk contributor to this system, while barley is the major risk receiver of shocks.

Complexity vs Empirical Score

• Math Complexity: 5.5/10
• Empirical Rigor: 7.5/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper provides a solid empirical assessment of dynamic risk spillovers across agriculture, energy, carbon, and climate markets using a recently proposed R2 decomposed connectedness methodology. It relies on standard daily futures data and comprehensive descriptive statistics, demonstrating strong backtesting and econometric validation, though the theoretical math relies largely on established PCA and VAR transformations.

Research Flowchart

  flowchart TD
    A[Research Goal: Analyze dynamic spillovers among Oil, Carbon, Climate, and Agriculture markets] --> B[Data Inputs: Time series for Crude Oil, Carbon Allowance, Climate Change, and Agricultural Commodities]
    B --> C[Methodology: Novel R² Decomposed Connectedness Approach]
    C --> D[Computational Process: Decompose Total Connectedness Index into Contemporaneous vs. Lagged Dynamics]
    D --> E[Key Finding 1: System dominated by contemporaneous rather than lagged dynamics]
    D --> F[Key Finding 2: Climate change exerts significant spillovers to other markets]
    D --> G[Key Finding 3: Heterogeneous agricultural impacts - Corn is top risk contributor; Barley is top receiver]