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Abstract
Accurate price forecasting for agricultural commodities can have significant decisionmaking
implications for suppliers, especially those of biofuels, where the agriculture
and energy sectors intersect. Environmental pressures and high oil prices affect
demand for biofuels and have reignited the discussion about effects on food prices.
Suppliers in the sugar–alcohol sector need to decide the ideal proportion of ethanol
and sugar to optimise their financial strategy. Prices can be affected by exogenous factors,
such as exchange rates and interest rates, as well as non-observable variables like
the convenience yield, which is related to supply shortages. The literature generally
uses two approaches: artificial neural networks (ANNs), which are recognised as being
in the forefront of exogenous-variable analysis, and stochastic models such as the Kalman
filter, which is able to account for non-observable variables. This article proposes
a hybrid model for forecasting the prices of agricultural commodities that is built upon
both approaches and is applied to forecast the price of sugar. The Kalman filter considers
the structure of the stochastic process that describes the evolution of prices.
Neural networks allow variables that can impact asset prices in an indirect, nonlinear
way, what cannot be incorporated easily into traditional econometric models.