Moment connectedness and driving factors in the energy-food nexus: A time-frequency perspective

TL;DR

This paper analyzes the complex, time-varying risk spillovers between energy and food markets using an integrated framework, identifying key drivers and implications for risk management and policy.

Contribution

It introduces a novel combined approach using GJRSK, time-frequency analysis, and random forests to study multidimensional risk spillovers in the energy-food nexus.

Findings

Significant multidimensional risk spillovers with time variation and heterogeneity.

Crude oil acts as a central transmitter in connectedness networks.

Multiple factors influence spillover effects, varying across timescales.

Abstract

With escalating macroeconomic uncertainty, the risk interlinkages between energy and food markets have become increasingly complex, posing serious challenges to global energy and food security. This paper proposes an integrated framework combining the GJRSK model, the time-frequency connectedness analysis, and the random forest method to systematically investigate the moment connectedness within the energy-food nexus and explore the key drivers of various spillover effects. The results reveal significant multidimensional risk spillovers with pronounced time variation, heterogeneity, and crisis sensitivity. Return and skewness connectedness are primarily driven by short-term spillovers, kurtosis connectedness is more prominent over the medium term, while volatility connectedness is dominated by long-term dynamics. Notably, crude oil consistently serves as a central transmitter in diverse connectedness networks. Furthermore, the spillover effects are influenced by multiple factors, including macro-financial conditions, oil supply-demand fundamentals, policy uncertainties, and climate-related shocks, with the core drivers of connectedness varying considerably across different moments and timescales. These findings provide valuable insights for the coordinated governance of energy and food markets, the improvement of multilayered risk early-warning systems, and the optimization of investment strategies.