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

A hybrid commodity price‐forecasting model applied to the sugar–alcohol sector Accurate price forecasting for agricultural commodities can have significant decision‐making 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Australian Journal of Agricultural Resource Economics Wiley

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

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Publisher
Wiley
Copyright
Copyright © 2011 Wiley Subscription Services, Inc., A Wiley Company
ISSN
1364-985X
eISSN
1467-8489
DOI
10.1111/j.1467-8489.2011.00534.x
Publisher site
See Article on Publisher Site

Abstract

Accurate price forecasting for agricultural commodities can have significant decision‐making 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.

Journal

The Australian Journal of Agricultural Resource EconomicsWiley

Published: Apr 1, 2011

Keywords: ; ; ;

References