A hybrid commodity price‐forecasting model applied to the sugar–alcohol sector

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.

A Forecast Combination Framework with Multi‐Time Scale for Livestock Products’ Price Forecasting

“…Accurate prediction provides a solid foundation for better planning and administration of agricultural products. e existing literature contributes a lot in improving the forecast accuracy by introducing sophisticated forecast models as well as optimizing the parameters within the models [18,31,32]. But, there is still a consensus in the forecasting literature that no single model is the best for all the cases [33,34].…”

“…Furthermore, the Diebold Mariano test is implemented in order to validate whether the difference between the two forecasts is statistically significantly different from zero [18]. e null hypothesis is that the two competing methods have the same forecast accuracy.…”

“…In the realm of forecast combination, most of the existing studies focus on combining different types of single models. For example, Ribeiro and Oliveira combined artificial neural networks and stochastic models to predict the price of sugar alcohol sector in Brazil [18]. Xiong et al combined the seasonal-trend decomposition model and extreme learning machines to predict the price of vegetables in China [19].…”

Analysis of Agricultural Commodities Prices with New Bayesian Model Combination Schemes

“…Ribeiro and Oliveira [14] noticed the important role of stocks quotas in predicting agricultural commodities prices. However, they provided an analysis confirming that the convenience yield can be a more important explanatory variable in modelling these commodities prices.…”

“…Unfortunately, such an approach omits the fact that during different periods, the "true" model might change. Except that, for traditional models it is harder to capture possible non-linearities [14,15]. Besides, there is a need for more developed econometric models, as the already applied ones usually have poor forecasting performance [16].…”