Storage, Convenience Yield, and Commodity Prices

In this article, we will understand the concepts of “storage” and “convenience” yield with reference to commodities and energy markets. We will also look at how storage and convenience yields impact the commodity prices.

Storage refers to physical holding of a commodity. By keeping an inventory of the commodity the holder of the commodity can meet unexpected demand without affecting their production process.

Convenience yield refers to the premium associated with holding the product or physical good. It is the implied yield (or return) from simply holding a commodity.

The concept of storage and convenience yield is closely related to each other.

According to Helyette Geman, convenience yield can be “defined as the difference between the positive gain attached to the physical commodity minus the cost of storage.” The convenience yield can be positive or negative. The yield will depend on the commodity, the length of time it is stored, and the level of inventory.

Storage and Commodity Prices

There is an inverse relationship between the commodity prices and storage levels. When the storage levels are low, the commodity prices tend to rise, and vice verse. This holds true for all commodities (both energy and non-energy commodities).

Similarly, volatility is also inversely related to storage. Commodity prices and volatility are positively related.

The storage generally impacts the short-term prices. Long-term pricing depends on other factors such as potential new energy to be discovered.

Understanding the concept of storage and convenience yield is important for any energy risk manager.

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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.