S&P Global Commodity Insights
Pricing Methodologies
Since 1909, S&P Global Commodity Insights has been evolving rigorous methodologies for data gathering, mathematical analysis and specialist judgement to create price assessments that reflect a commodity’s true value.
Human Systems
our-methodologies provide consistency and robustness in our price assessments, through principles and parameters that guide our market specialists every moment of every day.
For complex markets, these human systems are essential for assessing commodity market data, where automation alone can miss the rich texture and subtleties of commodities trading.
They’re embraced and implemented by our editorial team with relentless individual endeavour and unwavering focus on a global scale.
Decades of investment have led to the development of the most detailed, robust and varied methodologies available.
However, these systems are only brought to life by the extraordinary time and effort our people invest daily to bring transparency and fairness to the markets they serve.
We are price neutral and fully transparent
Providing pricing leadership that is fair to all of the market, we are price neutral, editorially independent and fully transparent with our-methodologies and assessment calculations. Absolute integrity remains fundamental to everything we do and underpins the symbiotic relationship we have with the markets we serve.
Close consultation is required to develop a methodology that meets a market’s specific needs.
Our specialists engage in ongoing dialogue with the market as it evolves, adapting our-methodologies based on market changes and feedback.
We have two different methodologies that govern how our market reporters gather and assess data.
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Market on Close
The S&P Global Commodity Insights Market on Close (MOC) methodology demands the greatest level of expertise and judgement from our market reporters because of the sheer depth of data they gather and assess.
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Index
Through our Index methodology market participants provide transactional data reports, often from a mid or back office, that our teams then analyze and collate into a rigorously tested volume weighted average.
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