The main methods used to help forecast oil prices include futures prices, fundamentals, the cost of production, the exchange rates of commodity-exporting countries, technical analysis, hotellings rule and finally todays price. This list is not exhaustive and by their nature are often used in combination with other methods and used in different ways over short and long-term forecasting periods.

Futures prices as a predictor

Futures prices are often used in the macroeconomic models built by central banks and other official agencies, and it is tempting to view them as a commodity price forecast. To recap, futures markets provide a means for trading the price of a commodity for delivery at some point in the future. The shape of this series of prices is known as the futures curve. The notion that the futures price is the best forecast of the spot price comes from a belief in the so-called “Efficient Market Hypothesis”. In an efficient market, new information is instantly reflected in commodity prices.

The two terms “futures” and “forecast” both sound like they should represent the same thing. They are anything but. A cursory review of the futures curve’s behaviour in recent years shows that it has been a very poor predictor of realised spot commodity prices. The futures curve shows the price at which it is possible to buy or sell contracts for a date in the future at a price agreed on today. It is not a forecast of future spot prices.

Fundamentals as a predictor

Constructing a supply and demand balance of current fundamentals and then adjusting them up or down over the outlook period is one way that institutions attempt to quantitatively predict whether a commodity market will be in deficit or surplus in the future. In this simple approach, forecasters might look in isolation at a number of economic variables to gauge what impact they have had on demand and supply in the past, and then adjust accordingly based on the forecaster’s expectations for the future. This can be a risky approach. Since commodity markets are characterised by price inelastic demand and supply, even small revisions in the expected path of future supply and demand can have large and volatile effects on prices.

Alternatively, forecasters typically rely on more complex data-based models to forecast commodity demand and supply. Models analyse the relationship between thousands of economic variables, from industrial production, business and consumer confidence data, retail sales volumes, money aggregates, and interest rates, data on inventories and production of industrial metals and energy commodities.

The cost of production as a predictor

Over the long term, the cost of production is thought by many to represent the best gauge for future commodity prices. Remember though that prices can, and frequently do fall below both average and marginal cost levels for considerable periods of time. Even if a particular mine is operating at a loss, there is a good chance it will continue to operate. This is because it costs a significant amount of money to close and eventually re-open a mine – so producers will tend to keep operating it for much longer than they would ideally want to. Once the initial investment has been made the incentive remains to continue producing as long as the price remains above the project’s operating cost. This will usually be much lower than the breakeven rate or marginal cost of production.

The exchange rates of commodity-exporting countries as a predictor

Some market observers believe that the exchange rate fluctuations of commodity-exporting economies – such as Australia, Chile or South Africa – are privileged predictors of future global commodity prices. Primary commodity products represent significant components of output in the above-mentioned countries, affecting a large fraction of their export earnings. Changes in the global commodity price of copper and iron ore represent significant external shocks for Chile and Australia, respectively. Therefore, their exchange rates should, in theory, move today in anticipation of the future terms of trade adjustment.

Technical analysis as a predictor

Many commodity futures speculators and forecasters base their analysis of the likely future path of commodity prices on technical analysis, ignoring underlying fundamentals. Physical buyers and sellers of commodities are increasingly using technical analysis to help guide their decisions. Technical analysis involves looking at the past performance of commodity prices to predict or at the least point to the potential risk of future price movements.

Hotelling’s rule as a predictor

Hotelling’s rule states that the most profitable extraction path for a non-renewable resource is one along which the price of the commodity, determined by the marginal net revenue from its sale, increases at the rate of interest. To use oil as an example, if a producer believed that prices would