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The transmission of oil price shocks has been a question of central interest in macroeconomics since the 1970s. There has been renewed interest in this question after the large and persistent fall in the real price of oil in 2014–16. In the context of this debate, Ramey (2017) makes the striking claim that the existing literature on the transmission of oil price shocks is fundamentally confused about the question of how to quantify the effect of oil price shocks.

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.

Because commodity prices help determine Canada’s terms of trade, employment, income and, ultimately, inflation, it is important to understand what causes them to fluctuate. Since the early 1900s, there have been four commodity price supercycles—which we define as extended periods of boom and bust that can take decades to complete. Now in its downswing phase, the current supercycle started after growth in China and other emerging-market economies in the mid-1990s resulted in an unexpected demand shock. The extent of this downswing depends on numerous factors that are presently uncertain.

Between mid-2014 and early 2016, oil prices fell by roughly 65 per cent. This note documents the channels through which this oil price decline is expected to affect the global economy. One important and immediate channel is through higher expenditures, especially in net oil-importing countries.

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.

This note investigates the relationship between crude oil prices and investment in the energy sector. We employ a set of vector autoregression (VAR) models (unconstrained VAR, vector error-correction and Bayesian VAR) to formalize the relationship between the West Texas Intermediate (WTI) benchmark and fixed-asset cash spending in the oil and gas extraction and support activities sector of the Canadian economy.

Futures markets are a potentially valuable source of information about price expectations. Exploiting this information has proved difficult in practice, because time-varying risk premia often render the futures price a poor measure of the market expectation of the price of the underlying asset.