Define and Compute a Commodity Spread

A spread position is one where trading takes places on many contracts on the same or related commodity. If the price between two related commodities changes then the idea is to profit from the opportunity.

There are two main types of commodity spreads

  1. Inter-commodity spread – In this case a position is taken in two different but related commodities.
  2. Intra-commodity spread – In this case the position is taken in different maturity months for the same commodity.

An example of this would be the case of wheat and corn which are used to ethanol production and cattle feed respectively. A certain relationship exists between the prices of these two commodities i.e. they must follow a certain pattern. Therefore it is easier to trade spread positions on them. These would then be inter-commodity spreads.

Another example would be the use of pork belly spreads because their prices are easily available in the market and one can expect them to go through reasonable price fluctuations. The normal spread is when the far contract is short sold and the nearby contract is purchased when the premium of the far to near is judged as being too great. These judgments are based on the ratio of premium to transactions and the carrying charges. The three main types of spreads that can occur are as follows:

  1. If the price premium judged is too high then according to this all possible combination of contracts should be spread.
  2. This is the same as number 1 except for the fact that only one spread is permitted per combination of contracts
  3. This is also the same as number 1 except for the maximum holding period is determined.

Examples

AT&T shares sell both on the Mid-West as well as the New York exchanges and the price difference in these two markets very rarely exceed that of the transaction costs in the purchase and sale of the security. If in case the spread does exceed the amount then there will be purchases on the exchange where it is cheapest and sales on the place where it is most expensive. When the buyer receives the stock he can cover the short sale and pocket the profit and this result in the risk-free arbitrage opportunity.

There are cases where there is shorting of the near contract and purchase of the long-term one which is called a reverse spread. Reverse arbitrage is of course very rarely possible.

  • The inter-commodity spread is formed by the following transactions. In February a July 1 wheat contract per bushel is sold and a July 1 corn contract per bushel is bought. The prices are 500 and 300 cents per bushel respectively. On June 1 wheat contract is bought and 1 July corn contract per bushel is sold at 300 and 200 cents respectively.

There are losses on corn and gain on wheat due to this.

The loss on corn is – 0.1 * 5000 = $500

The gain of wheat is – 0.2 *5000 = $1000

The total amount gained is $500.

  • Copper futures prices for three delivery months July, September and December are 57, 57.5 and 60.5 cents per pound. The butterfly spread on this is as follows:
    • On November 10 sell 1 July contract at 57 cents per pound and buy two September contracts at 57.5 per pound and sell one December contract for 60.5 cents per pound.
    • On April 15 buy one July contract at 55 cents per pound and sell two September contracts at 57 per pound and buy one December contract at 58.5 cents per pound.

The profits and losses for each of the delivery months are as follows:

July – 0.02 * 20000 = 400

September – (-0.005) *2 *20000 = -200

December – 0.02 * 20000 = 400

The total profit is the sum of the three which gives us $600.

This is an example of an intra-commodity spread.

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Data Science in Finance: 9-Book Bundle

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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
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  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

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