Oil Prices – A Barrel Of Texas Tea: Half-Full Or Half-Empty? by Jim Masturzo, Research Affiliates
- We develop a simple model to forecast the price of oil 12 months ahead, using four demand variables (return of copper, relative value of the USD, 10-year U.S. T-bond yield, and slope of the oil futures term structure) and two supply variables (U.S. oil production and OPEC oil production).
- Cyclical changes in production by both the United States and OPEC accounted for 2/3 of the more than 50% drop in the price of oil since the June 2014 peak at over $105 a barrel.
- Our model forecasts an oil price of $30–$40 a barrel in summer 2017. Investors seeking diversification and yield should consider commodities, selecting those, however, which are not heavily tilted toward oil.
“And up through the ground came a bubbling crude (oil, that is, black gold, Texas tea).” — Ballad of Jed Clampett
What direction are oil prices headed? Many investors have been seeking the answer to this question over the last 18 months. We show a straightforward model that captures the persistent supply and demand drivers of historic oil price moves. Then, tapping into history, we generate reasonable scenarios for oil prices, which indicate the downward price trend of recent years has yet to run its course.
Using our model, we fully expect West Texas Intermediate (WTI) oil prices to stay below $50, with the $30–$40 range a very reasonable and potentially best-case expectation over the coming 12 months. As asset allocators and not oil traders, our focus is less on identifying a specific price target for oil, but rather in understanding if prices are more likely to fall slightly or return quickly to the $80–$100 a barrel range. The two scenarios have very different ramifications across the variety of assets in our portfolios, and our model was developed with this purpose in mind.
An adage in commodities investing is that the cure for high prices is high prices, and the cure for low prices is low prices. High prices beget more mining, drilling, or agricultural land use, which increases supply and drives prices lower, whereas low prices drive resources away from these types of endeavors, forcing prices higher. The weakening in oil prices may already have started, stimulated by the supply glut reported in the July 2016 U.S. Energy Information Administration’s “Short-Term Energy Outlook” (STEO) report. Given our bearish outlook for oil prices over the next year, investors should consider filling their commodity allocations with those commodities not tilted heavily toward oil and oil-related products.
Drivers of Oil Prices
Investors who seek to understand the drivers of crude oil prices are greeted with much conjecture, and while many explanations may be valid over the very short term, they are not valid over the longer term. Because we are investors and not speculators, we take a longer-term view than just a couple of days or weeks in estimating the price of oil. Therefore, we analyze monthly oil prices back to the early 1980s in developing our model.
Our objective is to develop a simple model aimed at identifying systematic drivers of monthly changes in the spot price of WTI crude oil. As a first step we start with a simple return model applicable to any asset: An investor’s return from an asset is the sum of net cash flows received from the asset (oil is a non-cash-generating asset so net cash flows here are zero) plus the change in the asset’s price due to the forces of supply and demand:
Return = Net Cash Flows + Price Changes from Supply Forces + Price Changes from Demand Forces
Our goal is to identify the important and significant supply and demand forces responsible for the historical price fluctuations in oil. From a regression framework perspective, demand effects have been more commonly studied, and here we borrow from the research of Hamilton (2014) and Bernanke (2016) in developing our model. Their work focuses on weekly observations to make inferences about near-term changes in oil prices due to short-term changes in demand factors. Many of the drivers they use are helpful in our model, even at longer horizons.
Price Trend and Shocks
One method to explain oil prices is a model that includes all persistent supply and demand factors as independent variables to describe oil price changes. This model, however, masks some important information that can be extracted if we take a slightly different approach.
Instead of analyzing the oil price series as a whole, we separate historical oil prices into two components: 1) the trend and 2) the cyclical adjustments around the trend. We then analyze the cyclical adjustments as changes from trend, calling these shocks to trend. Often, cyclical changes are both explainable and expected, so use of the word “shocks” is not meant to imply a lack of expectation.
Our approach is borrowed from (Poghaosyan and Hesse, 2009) and makes use of the Hodrick–Prescott (HP) filter as a straightforward way to decompose an asset series into its trend and cyclical components. The Hodrick–Prescott filter is commonly used as a mechanism to decompose economic time series. Because oil prices are nonstationary, we can make use of the HP filter:
Oil Pricet = Trend Pricet + Cyclical Pricet
Shockt = Cylical Pricet / Trend Pricet
Oil Pricet = Trend Pricet (1 + Shockt)
The trend component captures secular changes in the asset price. An argument could be made whether the HP filter identifies the “correct” trend, but more important here is that the filter has the benefit of allowing a simple price decomposition. The price of oil at the end of June 2016 was $45.80, almost $9.00 above trend.
The trend in oil prices is heavily auto-correlated, so we can simply model the expected trend value in the subsequent period as the average return over the previous 12 months:
E[Trend Returnt+1] = Average (Trend Returnt,t-12)
Armed with this simple mechanism to describe the trend, the next step is to analyze the cyclical changes around it. Many of the factors influencing these changes do not stand out when we look at the full oil series, but become noticeable when we isolate the cyclical deviations from trend.
In the 30 years from January 1986 through June 2016, the average cyclical shock in the price of oil has been ?40 basis points (bps) with a large standard deviation close to 17%. Negative shocks tend to be more prevalent (55% versus 45%) than positive shocks, whereas positive shocks have historically been larger in magnitude.
Modeling Oil Shocks
Oil shocks occur due to cyclical changes in the supply and demand drivers of the market. The four demand factors we use in our model are the 1) price return of copper, 2) value of the U.S. dollar relative to a basket of other currencies (DXY Index), 3) yield of the 10-year U.S. Treasury bond, and 4) slope of the oil futures term structure as specified by the two nearest-to-maturity contracts.
Copper’s return is a proxy for global growth, and therefore global demand, and thus captures the pace of global expansion. Because oil is priced globally in U.S. dollars, changes in the dollar’s strength affect the underlying price of the commodity for market participants whose preferred currency