Are futures prices good price forecasts? Underestimation of price reversion in the soybean complex (2024)

Huang, J., Serra, T., & Garcia, P. (2020). Are futures prices good price forecasts? Underestimation of price reversion in the soybean complex. European Review of Agricultural Economics, 47(1), 178-199. https://doi.org/10.1093/erae/jbz009

Are futures prices good price forecasts? Underestimation of price reversion in the soybean complex. / Huang, Joshua; Serra, Teresa; Garcia, Philip.
In: European Review of Agricultural Economics, Vol. 47, No. 1, 01.02.2020, p. 178-199.

Research output: Contribution to journal › Article › peer-review

Huang, J, Serra, T & Garcia, P 2020, 'Are futures prices good price forecasts? Underestimation of price reversion in the soybean complex', European Review of Agricultural Economics, vol. 47, no. 1, pp. 178-199. https://doi.org/10.1093/erae/jbz009

Huang J, Serra T, Garcia P. Are futures prices good price forecasts? Underestimation of price reversion in the soybean complex. European Review of Agricultural Economics. 2020 Feb 1;47(1):178-199. doi: 10.1093/erae/jbz009

Huang, Joshua ; Serra, Teresa ; Garcia, Philip. / Are futures prices good price forecasts? Underestimation of price reversion in the soybean complex. In: European Review of Agricultural Economics. 2020 ; Vol. 47, No. 1. pp. 178-199.

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title = "Are futures prices good price forecasts? Underestimation of price reversion in the soybean complex",

abstract = "Using quantile regression, we evaluate the forecasting performance of futures prices in the soybean complex. The procedure provides a more complete picture of the distribution of forecasts than mainstream methods that only focus on central tendency measures. Forecast performance differs by location in the futures price distribution. Futures forecast perform well in the centre of the distribution. However, futures prices tend to over-forecast when futures prices are high and under-forecast when futures prices are low, suggesting that futures prices tend to under-estimate price reversion towards the centre of the distribution. Forecast errors are larger when futures prices are high. The findings are related to theories in the literature used to explain pricing bias, and their implications for market participants are discussed.",

keywords = "forecast, futures markets, quantile regression, soybean",

author = "Joshua Huang and Teresa Serra and Philip Garcia",

note = "Funding Information: The authors gratefully acknowledge economic support by the National Institute of Food and Agriculture, U.S. Department of Agriculture, Hatch under accession number ILLU-470-374 and the Office of Futures and Options Research (OFOR) at University of Illinois at Urbana-Champaign. We also thank anonymous referees for helpful comments. Publisher Copyright: {\textcopyright} 2019 Oxford University Press and Foundation for the European Review of Agricultural Economics 2019; all rights reserved.",

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N1 - Funding Information:The authors gratefully acknowledge economic support by the National Institute of Food and Agriculture, U.S. Department of Agriculture, Hatch under accession number ILLU-470-374 and the Office of Futures and Options Research (OFOR) at University of Illinois at Urbana-Champaign. We also thank anonymous referees for helpful comments.Publisher Copyright:© 2019 Oxford University Press and Foundation for the European Review of Agricultural Economics 2019; all rights reserved.

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N2 - Using quantile regression, we evaluate the forecasting performance of futures prices in the soybean complex. The procedure provides a more complete picture of the distribution of forecasts than mainstream methods that only focus on central tendency measures. Forecast performance differs by location in the futures price distribution. Futures forecast perform well in the centre of the distribution. However, futures prices tend to over-forecast when futures prices are high and under-forecast when futures prices are low, suggesting that futures prices tend to under-estimate price reversion towards the centre of the distribution. Forecast errors are larger when futures prices are high. The findings are related to theories in the literature used to explain pricing bias, and their implications for market participants are discussed.

AB - Using quantile regression, we evaluate the forecasting performance of futures prices in the soybean complex. The procedure provides a more complete picture of the distribution of forecasts than mainstream methods that only focus on central tendency measures. Forecast performance differs by location in the futures price distribution. Futures forecast perform well in the centre of the distribution. However, futures prices tend to over-forecast when futures prices are high and under-forecast when futures prices are low, suggesting that futures prices tend to under-estimate price reversion towards the centre of the distribution. Forecast errors are larger when futures prices are high. The findings are related to theories in the literature used to explain pricing bias, and their implications for market participants are discussed.

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