Uncertainty impacts on China's agricultural commodity futures: a quantile perspective

This study employs rolling quantile regression and non-parametric estimation methods, specifically Quantile-on-Quantile (QQ) and Causality-in-Quantiles (QC), to investigate the impact of uncertainty indices (Geopolitical Uncertainty (GPR), Climate Policy Uncertainty (CPU), and Economic Policy Uncertainty (EPU)) on China's agricultural commodity futures. The rolling window quantile regression coefficients vary over time, with CPU having the most significant impact on agricultural commodity futures. The empirical results from the non-parametric estimation QQ method reveal when agricultural commodities are in a prosperous market, CPU and EPU exhibit relatively strong correlation, while GPR shows weaker or insignificant correlation. According to the non-parametric estimation QC method, the evidence suggests that the second-order (variance) causality relationship is relatively stronger than the first-order (mean) causality relationship, and the first-order mean causal relationship between CPU and corn and between EPU and wheat is not significant across the entire range of quantiles. The results on Hedging Effectiveness reveal that the efficiency of uncertainty hedging is substantiated. The hedging effectiveness of EPU exhibits improvement post-COVID-19, whereas the effectiveness of GPR and CPU is more pronounced in the pre-COVID-19 period.