As noted in the Atlas Research Innovations teleconnections and food security papers, global agricultural risks are numerous in cause and effect, and geographic exposure is growing. Further, these risks are complex in the sense that they may have one or several unique catalysts. When all is said and done, every human on the planet needs to eat, so it is no overestimation when we discuss food security in terms of a global issue. Identifying potential supply-side risks and implementing appropriate responses, both physical and financial, will require a new lens through which to view risk to markets, trade and society at a foundational level.
Therefore, a more complete understanding of the global commodity supply chain is needed if we are to anticipate and manage risks to the world’s food supply. This Atlas Research Innovations paper discusses the framework around an alternative method to view agriculture risk: Alternative data in Agricultural Risk Management (AARM). Most risk managers employ somewhat traditional methods when trying to speculate stocks and flows of supply, and associated price movements. As such, most are also using the same data for their analyses and projections, and if there is a supply event that is not considered ‘normal’, they cannot assess potential market reactions ahead of time. We advocate for the use of a different set of tools. The identification and quantification of raw material exposures by commodity sector using non-traditional data and analysis allows for early signal detection towards identifying potential opportunities and managing risk related to agricultural commodity market activity.
The AARM strategy was developed as a means to manage physical commodity risk and the related market impact through (a) alternative data exploitation and (b) the employment of alternative methodologies. Standard risk management techniques are not sufficient to monitor and anticipate for commodity volatility, and this precipitated the development of the AARM strategy. The AARM strategy has been since 2001, and continues to improve as new data and methods become available.
This article is cross posted on Medium.
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