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Price Forecasting Through Multivariate Spectral Analysis: Evidence for Commodities of BMeFbovespa

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  • Carlos Alberto Orge Pinheiro

    (University of State of Bahia - UNEB)

  • Valter de Senna

    (Integrate Campus of Manufacture and Technology)

Abstract

This study aimed to forecast the prices of a group of commodities through the multivariate spectral analysis model and compare them with those obtained by classical forecasting and neural network models. The choice of commodities such as ethanol, cattle, corn, coffee and soy was due to the emphasis in the exports in 2013. The multivariate spectral model has proved to be suitable, when compared with others, by enabling a better predictive performance. The results obtained in the out-of-sample period, through the use of measurement error and statistical test, confirm this. This research may help market professionals in formulating and implementing policies targeted to the agricultural sector due to the relevance of price forecast as a planning instrument and analysis of the finance market behavior for those who need protection against price fluctuations.

Suggested Citation

  • Carlos Alberto Orge Pinheiro & Valter de Senna, 2016. "Price Forecasting Through Multivariate Spectral Analysis: Evidence for Commodities of BMeFbovespa," Brazilian Business Review, Fucape Business School, vol. 13(5), pages 129-157, September.
    • Handle: RePEc:bbz:fcpbbr:v:13:y:2016:i:5:p129-157
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    References listed on IDEAS

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    Cited by:

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