Fluctuations in Metals and Agricultural Commodity Markets Under US-China Trade Tension: Evidence from Quantile-on-Quantile Regression and Wavelet Coherence

This study investigates the ripple effects of U.S. and China trade tension (UCTI) on metals (both precious and industrial) and agricultural commodities, employing the Quantile-on-Quantile regression approach and the wavelet coherence technique. The empirical analysis is based on a monthly dataset covering the period from January 2010 to February 2024. The results reveal that U.S. and China trade tension negatively affects the returns of metals and agricultural commodities. At lower quantiles, silver, platinum, palladium, and copper returns exhibit a negative association with UCTI, while gold exhibits a weak positive association. At middle and higher quantiles, silver and platinum respond positively to U.S. and China trade tensions, whereas palladium reacts negatively at middle and middle-higher quantiles. For agricultural commodities, U.S. and China trade tensions negatively impact the returns of canola and corn at lower quantiles. However, oats, wheat, and soybeans exhibit a positive association at middle and higher quantiles. For frequency analysis findings further reveal that oats and canola are positively correlated with trade tensions at lower frequencies. Interestingly, at lower frequencies, canola, oats, corn, wheat, and soybeans lead U.S. and China trade tensions, while at higher frequencies, the direction reverses, with trade tensions leading these commodities. These findings are particularly meaningful through the lens of the U.S. and China trade tension, offering insights for policymakers, investors, and portfolio managers aiming to manage trade-related risk and seeking to optimize portfolio strategies amid geopolitical uncertainty.