BackSaw an oil price forecast? Junk it
Study says that even the most sophisticated econometric models often fail to predict big price swings, like the crude oil price jump in the recent years
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“The price of oil has fallen by half in the past two years, to just over $10 a barrel. It may fall further—and the effects will not be as good as you might hope," predicted Economist newspaper in March 1999, and we all know what happened next. Crude prices rose more or less steadily thereafter, reaching a peak of close to $130 a barrel just before the Great Recession.
The road to predicting oil prices is fraught with pitfalls and epitaphs of forecasters. But that hasn’t stopped anyone from trying to predict future prices of a commodity that is often at the heart of geopolitical tussles and can contribute to inflation or trigger deflation. While there has been some modicum of success, generally, it is the big moves in the markets that we want to predict—and fail to do so.
There is little hope that such an algorithm which can predict these big moves can be found soon. Research by Christiane Baumeister (University of Notre Dame) and Professor Lutz Kilian of University of Michigan reveals that even the most sophisticated econometric models often fail to predict big swings.
To be fair, such econometric models generally fare better than other ways of forecasting crude oil prices. Their paper characterizes forecasts of oil prices into four categories: One, economists using econometric models; two, financial markets, using futures data and adjusting for risk premium; three, consumer expectations based on inflation expectations; and four, policymakers’ forecasts often based simply on futures markets.
Data shows that the models of economists have fared better than others in predicting crude oil prices, in the period between 1988 and 2014 (CHART 1). However, when it comes to predicting big moves like the sharp drop in oil price from around $120 per barrel in July-September 2008 to average $60 per barrel in October-December 2008, almost all methods failed by quite big margins (CHART 2).
For example, chart 2C shows that the actual oil price in the December quarter of 2014 was more than 20% below what the markets, consumers and policymakers estimated at the beginning of the quarter. Hence, the oil price shock is shown to be around negative 20% when compared to such forecasts. Of course, the oil price shock seems a bit smaller (at negative 10% for October-December 2014) when compared to the econometrician’s forecast, thereby suggesting that economists fared slightly better in forecasting. However, the inaccuracy of forecast by the econometric model only worsened in January-March 2015 despite the fact that the direction of actual oil price movement did not change in the quarter.
So why is it so difficult to predict movements in oil prices, despite there being reasonable understanding of the drivers behind oil prices? The answer lies in the fact that while we may be able to identify what drives oil prices, it is difficult to predict those drivers itself. As the research paper itself concludes, “the price of oil will only be as predictable as its determinants."
For example, it is easy to say that oil prices generally fall in a rising interest rate environment, but it’s difficult to predict when interest rates will start rising and thereby hurt the ability of market participants to store oil (as interest-adjusted storage costs rise), leading sellers to release their stored oil, thereby resulting in a fall in prices.
The bad news, from the point of view of oil price forecasting, is that things might get even worse from here. With a number of developing countries, especially China, increasingly becoming more important in the global oil market, availability and reliability of relevant oil-related data from these countries might prove to be another bottleneck.
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