@article{Ribeiro:176895,
      recid = {176895},
      author = {Ribeiro, Celma O. and Oliveira, Sydnei M.},
      title = {A hybrid commodity price-forecasting model applied to the  sugar–alcohol sector},
      journal = {Australian Journal of Agricultural and Resource Economics},
      address = {2011},
      number = {428-2016-27903},
      pages = {19},
      year = {2011},
      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.},
      url = {http://ageconsearch.umn.edu/record/176895},
      doi = {https://doi.org/10.22004/ag.econ.176895},
}