Food volatility from Open Source

The historical volatility (20-day annualized) in percentages of a few traded agricultural commodities over many years.

Some simple statistics tabulated below:

The raw data was obtained as continuous second-month futures from quandl. A lot of interesting insights can be gleaned from this and many hypotheses can be tested from such simple analyses. Glaring is the vols of coffee and sugar; If you are a sweet coffee addict make sure you allocate extra budget towards this habit in the future!

While inedible and intangible things like Fx trade much lower vols comparatively, I think there should be a policy mandate to curtail volatility of such important consumables. With a predicted world population around the 10 Bil mark by 2050, volatility only seems like looking north for food and beverages.

While the exercise was done with the primary intent to bring about a perspective in the global food industry, it was also undertaken to showcase the power and simplicity of the power of open source programming. The above was done with about 30 lines of python code (which even school kids are learning these days).

Originally published at https://www.linkedin.com.