Quantile Connectedness and Time-Frequency Co-Movement Between Climate Policy Uncertainty And Grains Commodity Markets

The purpose of this study is to examine the dynamic association between the US Climate Policy Uncertainty Index (CPU) and grain commodity market prices from 2000:M1 to 2021:M12. As the spillover effect magnitude may switch between different market states over time, we utilise a quantile VAR (QVAR) model based on a rolling window to investigate the return spillover between the CPU and the grain commodity market at different quantile levels. We further examine the time-frequency causality of returns between the CPU and the grain commodity market using wavelet coherence techniques. Our results indicate that quantile connectedness is stronger at extreme levels than at median levels, underscoring the value of systematic risk spillovers at extreme market conditions. Interestingly, we find higher connectedness in the extreme upper tails than in the lower tails. Our wavelet coherence analysis suggests that the structure of the CPU correlation with the grain commodity market is heterogeneous at different time-frequency scales and is deeply embedded in the short- and medium-term frequency domains. Results from this study have valuable implications for investors in portfolio diversification and will also assist policymakers in developing countries to develop a sensible policy package