Big Data links from Climate to Commodity Production Forecasts and Risk Management

TL;DR

This paper investigates how climate data can improve orange production forecasts and influence commodity trading decisions, aiming to reduce forecast uncertainty and enhance risk management in the FCOJ market.

Contribution

It introduces models that incorporate climate variables to predict USDA forecast errors, potentially improving decision-making and forecast accuracy for orange production.

Findings

Climate variables can help reduce forecast error uncertainty.

Probabilistic forecasts influence trading strategies.

Models enable assessment of climate impact on orange production forecasts.

Abstract

Frozen concentrated orange juice (FCOJ) is a commodity traded in the International Commodity Exchange. The FCOJ future price volatility is high because the world's orange production is concentrated in a few places, which results in extreme sensitivity to weather and disease. Most of the oranges produced in the United States are from Florida. The United States Department of Agriculture (USDA) issues orange production forecasts on the second week of each month from October to July. The October forecast in particular seems to affect FCOJ price volatility. We assess how a prediction of the directionality and magnitude of the error of the USDA October forecast could affect the decision making process of multiple FCOJ market participants, and if the "production uncertainty" of the forecast could be reduced by incorporating other climate variables. The models developed open up the opportunity to assess the application of the resulting probabilistic forecasts of the USDA production forecast error on the trading decisions of the different FCOJ stakeholders, and to perhaps consider the inclusion of climate predictors in the USDA forecast.