Contango and Backwardation in Commodity Markets

In the commodity markets, the relationship between spot prices and futures prices is explained using the concept of contango and backwardation.

Contango: Refers to the situation where the futures prices are higher than the current spot prices.

Backwardation: Refers to the situation where the futures prices are lower than the current spot prices.

Contango is a sign that the market participants are expecting the spot prices to rise in the future. On the other backwardation means that market participants expect the spot prices to drop in the future.

As an example, the oil prices exhibit contango, as the consumers of oil drive the prices up. However, the futures price cannot vary widely from the current spot price because a very wide variation could give rise to arbitrage opportunities. Imagine the futures prices for gold are very high compared to spot prices. In that case, an investor has a pure arbitrage opportunity, as he could buy the gold in the spot market, store it, and sell it in the forward market, hence making a clean profit. This is called a carry trade, and the futures price of such a commodity will limited by the value of full carry (i.e., the storage cost and borrowing costs). However, this rule won’t apply to all commodities, as not all commodities can be stored for a long time, and there could also be limitations on how much can be stored.

In the earlier times, many commodities used to have natural backwardation, as the producers of commodities would want to sell their products at lower prices. Backwardation can occur if the markets have oversupply situations and the buyers are able to dictate the prices. In the past Crude oil and Natural gas used to be in backwardation.

Even though some people consider backwardation as an abnormal activity resulting from supply inefficiencies, the economist John Maynard Keynes argued that in commodity markets, backwardation is not abnormal, but rather arises naturally from the fact that producers of commodities are more prone to hedge their price risk than consumers.

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Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.