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.
Staff analytical notes
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.
In the second half of 2014, oil prices experienced a sharp decline, falling more than 50 per cent between June 2014 and January 2015. A cursory glance at this oil price crash suggests similarities to developments in 1986, when the price of oil declined by more than 50 per cent, initiating an episode of relatively low oil prices that lasted for more than a decade.
Staff working papers
Hurricanes disrupt oil production in the Gulf of Mexico because producers shut in oil platforms to safeguard lives and prevent damage. We examine the effects of these temporary oil supply shocks on real economic activity in the United States.
Do technological improvements mitigate the potential damages from extreme weather events? We show that hurricanes lower offshore oil production in the Gulf of Mexico and that stronger storms have larger impacts. Regulations enacted in 1980 that required improved offshore construction standards only modestly mitigated the production losses.
How can we assess the quality of a forecast? We propose a new benchmark to evaluate forecasts of temporally aggregated series and show that the real price of oil is more difficult to predict than we thought.
We quantify the reaction of U.S. equity, bond futures, and exchange rate returns to oil price shocks driven by oil inventory news.
This paper presents a structural framework of the global oil market that relies on information on global fuel consumption to identify flow demand for oil. We show that under mild identifying assumptions, data on global fuel consumption help to provide comparatively sharp insights on elasticities and other key structural parameters of the global oil market.
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.
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.
Bank of Canada Review articles
November 16, 2017 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.