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This study develops a Global Vector Autoregression (GVAR) model to simulate various types of shocks to oil markets and to see whether such shocks are time-sensitive in oil markets. Our model extends the canonical Mohaddes and Pesaran (2016) model temporally (to 2018Q3), spatially (including Russia, Iran, and Venezuela), and by adding oil inventories as an additional country-specific variable. Two of its characteristics make GVAR particularly suited to this analysis. First, the GVAR framework is specifically designed to account for the interaction between many countries. Second, world oil supplies and inventories are modeled jointly with key global and country-level macroeconomic variables. The results indicate conditions existing in the markets prior to the disturbance determine the global economic implications of an oil price shock. To cite only one example, a negative price shock in markets characterized by …