Quantile Connectedness and Tail Risks: Interactions between Energy and Agricultural Markets

This study examines the return and volatility spillovers, as well as tail-risk dynamics, between energy and agricultural commodity markets by analyzing the quantile connectedness of a system comprising key agricultural and energy commodities under extreme market conditions. We utilize a quantile vector autoregression (QVAR) model to show differences in the total connectedness index across varying market conditions and across time. Our findings show asymmetric returns spillovers between the commodities of interest, showing distinct risk transmission effects. In extreme market conditions, both bullish and bearish, we found the network connectivity of returns to be significantly stronger than under the median quantile, which represents normal market conditions. We also find under extreme scenarios, energy commodity markets tend to be more net transmitters, while the energy markets are net receivers of shocks. Our findings have implications for investors in risk management and portfolio diversification, as well as policymakers looking to manage commodity risk.