This note presents a structural vector autoregressive (SVAR) model of the global oil market. The model identifies four types of shocks with different economic interpretations: oil supply shocks, oil-market-specific demand shocks, storage demand shocks and shocks to global economic growth.
This paper shows that changes in market participants’ fear of rare events implied by crude oil options contribute to oil price volatility and oil return predictability. Using 25 years of historical data, we document economically large tail risk premia that vary substantially over time and significantly forecast crude oil futures and spot returns.
Oil prices have declined sharply over the past three years. While both supply and demand factors played a role in the large oil price decline of 2014, global supply growth seems to have been the predominant force. The most important drivers were likely the surprising growth of US shale oil production, the output decisions of the Organization of the Petro-leum Exporting Countries and the weaker-than-expected global growth that followed the 2009 global financial crisis.
In this note, we present the Commodities Factor Model (CFM), a dynamic factor model for a large cross-section of energy and non-energy commodity prices. The model decomposes price changes in commodities into a common “global” component, a “block” component confined to subgroups of economically related commodities and an idiosyncratic price shock component.
It is commonly believed that the response of the price of corn ethanol (and hence of the price of corn) to shifts in biofuel policies operates in part through market expectations and shifts in storage demand, yet to date it has proved difficult to measure these expectations and to empirically evaluate this view.