Factors Affecting Commodity Market Prices

Cash prices are derived from the futures markets by removing the effect of the cost of carrying the commodity i.e. by stripping out the financial cost of carry price. The driving factors behind the volatility in the prices of the commodities’ cash prices arises because they have different characteristics than financial products.

Factors that affect commodity prices

  1. Production related – Commodities are capital-intensive products i.e. they are influenced by natural factors like weather conditions, crop diseases, size of land cultivated and factors related to production like labor patterns, development in the tools and technologies used. Other than these there are factors like the economic and political environment which manifest itself in the form of trade constraints, subsidies, taxes to mention a few. Altogether these factors affect the cost of producing the commodity and the demand for it in a market where there is more than one participant.
  2. Storage and Transportation constraints – All commodities have a real physical form and therefore there is a need for storage prior to distribution. This is not the case of financial products so inventory cost and storage do not have such a large impact on the market prices. This factor does not however affect the prices across all commodity asset classes in the same magnitude but rather depends on the type of commodity in question.
  3. Economic and Demand related patterns – In recent times the uncertainties in the global financial system have made commodities a favorable investment alternative to financial instruments.  Typical examples would be gold and silver. The increasing involvement of  developing markets as suppliers expose the prices to the political and production related constraints in these countries like economic policy, infrastructure and labor conditions. This sometimes pushes prices higher.
  4. Costs involved in storage – There are two types of costs involved in storing commodities. One is the financial cost and the other is the cost of physical storage and they both need to be factored in when computing the forward prices.
  5. Seasonality – Some examples of such factors include weather related patterns, operational risk, climatic conditions and politics.

Commodities Compared

  • Gold – Storage costs are low due to increased durability. There is a lease rate implied in practice. There are two situations. Direct ownership or a synthetic long position. Direct ownership means there is no cost of leasing but storage cost is involved. A synthetic long position means there are no storage costs but there is credit risk exposure.
  • Corn – It is an agricultural commodity it is easily perishable and therefore the price is highly affected by seasonal factors like harvests and yearly weather conditions. These directly influence the storage costs and supply side respectively.
  • Natural Gas – Demand for gas is more in winter than in other seasons. Also the transportation and storage costs are higher than most other commodity types.  The following comparison should explain this characteristic: Agricultural products depend more on supply side rather demand changes as compared to gas which is affected more by demand rather than supply factors.
  • Oil – It is less expensive to transport and easier to store than natural gas.
  • Electricity – Supply side factors like demographics of land and distribution of population, availability of natural resources, political, economic environment and trade policies, type of economy (developed, semi-developed, underdeveloped)  mainly have an influence on the price and availability of this commodity. This is a resource that is required world-wide.

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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.