Unveiling unspanned macroeconomic factors in oil futures price dynamics: Evidence from China's oil futures market

Abstract

Global energy security and the stability of industrial chains increasingly hinge on the accuracy of crude oil futures price (COFP) discovery. Traditional models often overlook the unspanned macroeconomic factors (UMFs), which represent latent risks existing outside the immediate pricing information of futures contracts yet remain critical for long-term strategic planning. Therefore, the UMFs are extracted through principal component analysis, with bootstrapping employed to mitigate potential endogeneity concerns. Subsequently, a time-varying parameter vector autoregression (TVP-VAR) model is developed to investigate the dynamic interactions between UMFs and COFPs. To validate the framework, we conduct an empirical analysis of the Shanghai crude oil futures (SC) market. The results demonstrate that CPI, industrial added value and the U.S. economic policy uncertainty enhance the predictive accuracy of oil futures returns, particularly during extreme events. The effects of macroeconomic conditions on oil prices are both lagged and time-dependent. Robustness checks further confirm that UMFs capture this delayed relationship more effectively. This underscores their key role in explaining how changes in the macroeconomy affect SC prices over time. Furthermore, SC prices demonstrate significant lagged and asymmetric effects on the macroeconomy, whereby price decreases exert considerably stronger impacts than increases, suggesting that oil-importing nations like China benefit disproportionately from falling prices. We provide insights into the mechanisms linking the macroeconomy and the emerging market.