The price of crude oil is volatile. Our first chart shows the price of a barrel of Brent crude since the early 1970s, adjusted for inflation. In real terms, oil prices, which had been broadly stable for more than a decade, rose by a factor of six between the late 1990s and the late 2000s, only to plummet as the global financial crisis hit, rebound as the recovery began, and fall sharply again through 2014 and 2015 as investors rightly became concerned by the pace of China’s slowdown. At the time of writing, Brent crude is trading around $75 a barrel, somewhat below levels seen a few months ago. In this week’s News in Charts we consider recent movements in the price of crude oil in the context of Fathom’s oil-market model. We find that, if our judgement that we are heading for a global recession in 2020 turns out to be broadly correct, oil could be approaching $20 a barrel within a year or two.
Refresh the chart in your browser | Edit chart in Datastream
In principle, changes in the price of oil can be very informative about both current and prospective changes in economic activity. A large increase in the price of oil might be telling us that traders in the oil market expect a large pickup in global economic activity. But that is only true if the increase in the price of oil reflects an anticipated increase in demand. If instead it reflects an anticipated reduction in supply, then the converse is true. Oil, or rather energy more broadly, can be regarded — alongside land, labour and capital — as one of the factors of production. A reduction in the supply of oil is likely to reduce global productive potential, and with it global economic activity. That means it is crucial, when interpreting oil price movements, to know whether price changes reflect a change in oil demand, or a change in oil supply. At Fathom, we have developed a simple statistical model that helps us do that.
In 1989 Olivier Blanchard and Danny Quah proposed a novel technique for identifying shocks to aggregate demand and aggregate supply in the US. They used a simple model known as a vector autoregression (or VAR), that contained just two variables — GDP growth and the unemployment rate — which depended only on lags of themselves and each other. By imposing the restriction that only shocks to aggregate supply could have a permanent effect on the level of GDP — while allowing for the possibility that shocks to aggregate demand might have a temporary effect — they produced time series for shocks to aggregate demand and supply. We have applied the same technique to the oil market. Our two variables are the growth in global oil consumption, and the real price of oil. The restriction we apply is that only shocks to oil supply can have a permanent effect on the level of oil consumption. The demand and supply shocks that we obtain are shown in the chart below.
Refresh the chart in your browser | Edit chart in Datastream
Want more charts and analysis? Access a pre-built library of charts built by Fathom Consulting via Datastream Chartbook in Thomson Reuters Eikon.
The model identifies large negative shocks to oil supply in 1974, then again in 1979 and 1980. In October 1973, the Organisation of Petroleum Exporting Countries (OPEC) put in place an embargo targeted at the US and other major oil-consuming nations which led to the price of oil rising by a factor of three. This became known as the first oil crisis. The second oil crisis followed in 1979, the year of the Iranian revolution, which saw global oil production fall by 4%. Prices remained elevated through 1980 with the outbreak of the Iran–Iraq war. This second oil price shock led to a recession in many major economies.
Looking at the more recent past, the model identifies positive shocks to demand in all but three of the seventeen years since China’s accession to the WTO. The three exceptions were 2008 (when the global financial crisis hit), 2014 and 2015. In these latter two years China’s economy began to slow dramatically. Although that slowdown was not reflected in China’s official GDP statistics, it was captured by our China Momentum Indicator, and it caused sharp falls in the prices of many other commodities central to China’s production process, such as steel. Our model can be used to decompose changes in the real price of oil into an amount due to changes in demand and an amount due to changes in supply. It finds that almost all the variation in the real price of oil since China’s accession to the WTO has been driven by shocks to demand — and in our judgement, most of these, the global financial crisis aside, can be pinned on China.
Refresh the chart in your browser | Edit chart in Datastream
We can use our model to estimate the impact on the real price of oil of possible future changes in economic activity. To do this, we need to make an assumption about the level of economic activity that is priced in to the oil market at the moment. Let us assume that the consensus view is close to that of the IMF — it often is. As clients will be aware, we see a significant risk of a global recession in 2020, whereas the IMF expects little slowdown in the pace of economic activity. What if we are right and the IMF is wrong? More specifically, what if the consensus begins to switch at some point next year from the IMF forecast for global growth towards ours? That would mean that forecasts for the level of global GDP in 2020 would fall by some 3% relative to what is currently priced in. Using our oil market model, the implied reduction in demand would see oil prices fall towards $20 a barrel by the middle of 2020.
______________________________________________________________________
Thomson Reuters Datastream
Financial time series database which allows you to identify and examine trends, generate and test ideas and develop view points on the market.
Thomson Reuters offers the world’s most comprehensive historical database for numerical macroeconomic and cross-asset financial data which started in the 1950s and has grown into an indispensable resource for financial professionals. Find out more
