IDEAS home Printed from https://ideas.repec.org/p/ags/iamo10/90802.html

How Predictable are Prices of Agricultural Commodities? – The Possibilities and Constraints of Forecasting Wheat Prices

Author

Listed:
  • Holst, Carsten

Abstract

Wheat price forecasts are very important for traders, farmers and politicians as well. However, only quite accurate price predictions can guide these groups towards making the best decisions. Therefore the well-known wheat price projections of both the OECD and the FAPRI from 1996 on are tested for their predictive accuracy using Theil’s inequality coefficient. Despite the fact that both models could not foresee the price peak which occurred in February 2008, their predictions offer more accurate values than a naive prediction of no price change. Nevertheless, precise price forecasts cannot be expected by the models of the OECD and the FAPRI since some short-run effects such as inappropriate weather are not predictable. Thus, our own econometric model is developed taking the previous price development, the stocks-to-use-ratio and the crude oil price into account. In comparison to the projections of both institutions the model, with rather simple assumptions, was able to generate forecasts more accurately. In a simulation study which takes different crude oil price levels and stochastic effects of the world wheat consumption and the average yields per hectare into account, the possible wheat price range is shown as large. Therefore, price predictions can only inform about general long-run trends.

Suggested Citation

  • Holst, Carsten, "undated". "How Predictable are Prices of Agricultural Commodities? – The Possibilities and Constraints of Forecasting Wheat Prices," 2010 IAMO Forum, June 16-18, 2010, Halle (Saale), Germany 90802, Institute of Agricultural Development in Transition Economies (IAMO).
  • Handle: RePEc:ags:iamo10:90802
    DOI: 10.22004/ag.econ.90802
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/90802/files/Holst_IAMO_Forum%202010.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.90802?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Brümmer, Bernhard & Koester, Ulrich & Loy, Jens-Peter, 2008. "Tendenzen auf dem Weltgetreidemarkt: anhaltender Boom oder kurzfristige Spekulationsblase?," DARE Discussion Papers 0807, Georg-August University of Göttingen, Department of Agricultural Economics and Rural Development (DARE).
    2. Dwight R. Sanders & Scott H. Irwin, 2010. "A speculative bubble in commodity futures prices? Cross‐sectional evidence," Agricultural Economics, International Association of Agricultural Economists, vol. 41(1), pages 25-32, January.
    3. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Holst, Carsten, 2010. "How predictable are prices of agricultural commodities? The possibilities and constraints of forecasting wheat prices," IAMO Forum 2010: Institutions in Transition – Challenges for New Modes of Governance 52717, Leibniz Institute of Agricultural Development in Central and Eastern Europe (IAMO).
    2. Hauzenberger, Niko & Huber, Florian & Klieber, Karin & Marcellino, Massimiliano, 2025. "Bayesian neural networks for macroeconomic analysis," Journal of Econometrics, Elsevier, vol. 249(PC).
    3. Laurence T Kell & Iago Mosqueira & Henning Winker & Rishi Sharma & Toshihide Kitakado & Massimiliano Cardinale, 2024. "Empirical validation of integrated stock assessment models to ensuring risk equivalence: A pathway to resilient fisheries management," PLOS ONE, Public Library of Science, vol. 19(7), pages 1-21, July.
    4. Anatoly A. Peresetsky & Ruslan I. Yakubov, 2017. "Autocorrelation in an unobservable global trend: does it help to forecast market returns?," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 7(1/2), pages 152-169.
    5. Kelly Burns & Imad Moosa, 2017. "Demystifying the Meese–Rogoff puzzle: structural breaks or measures of forecasting accuracy?," Applied Economics, Taylor & Francis Journals, vol. 49(48), pages 4897-4910, October.
    6. Yuchen Zhang & Shigeyuki Hamori, 2020. "The Predictability of the Exchange Rate When Combining Machine Learning and Fundamental Models," JRFM, MDPI, vol. 13(3), pages 1-16, March.
    7. Hao Chen & Qiulan Wan & Yurong Wang, 2014. "Refined Diebold-Mariano Test Methods for the Evaluation of Wind Power Forecasting Models," Energies, MDPI, vol. 7(7), pages 1-14, July.
    8. Antonello D’Agostino & Kieran Mcquinn & Karl Whelan, 2012. "Are Some Forecasters Really Better Than Others?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(4), pages 715-732, June.
    9. Gaglianone, Wagner Piazza & Guillén, Osmani Teixeira de Carvalho & Figueiredo, Francisco Marcos Rodrigues, 2018. "Estimating inflation persistence by quantile autoregression with quantile-specific unit roots," Economic Modelling, Elsevier, vol. 73(C), pages 407-430.
    10. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Post-Print halshs-01442618, HAL.
    11. Guglielmo Maria Caporale & Juncal Cuñado & Luis A. Gil-Alana, 2013. "Modelling long-run trends and cycles in financial time series data," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(3), pages 405-421, May.
    12. Jesus Crespo Cuaresma & Ines Fortin & Jaroslava Hlouskova & Michael Obersteiner, 2024. "Regime‐dependent commodity price dynamics: A predictive analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2822-2847, November.
    13. Carlo Altavilla & Paul De Grauwe, 2010. "Forecasting and combining competing models of exchange rate determination," Applied Economics, Taylor & Francis Journals, vol. 42(27), pages 3455-3480.
    14. Matsumura, Marco & Moreira, Ajax & Vicente, José, 2011. "Forecasting the yield curve with linear factor models," International Review of Financial Analysis, Elsevier, vol. 20(5), pages 237-243.
    15. N. Antonakakis & J. Darby, 2013. "Forecasting volatility in developing countries' nominal exchange returns," Applied Financial Economics, Taylor & Francis Journals, vol. 23(21), pages 1675-1691, November.
    16. Castro, Luciano de & Galvao, Antonio F. & Kim, Jeong Yeol & Montes-Rojas, Gabriel & Olmo, Jose, 2022. "Experiments on portfolio selection: A comparison between quantile preferences and expected utility decision models," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 97(C).
    17. Bagsic, Cristeta & Paul, McNelis, 2007. "Output Gap Estimation for Inflation Forecasting: The Case of the Philippines," MPRA Paper 86789, University Library of Munich, Germany.
    18. Tom Boot & Bart Keijsers, 2025. "Diffusion index forecasts under weaker loadings: PCA, ridge regression, and random projections," Papers 2506.09575, arXiv.org.
    19. Vitek, Francis, 2006. "Measuring the Stance of Monetary Policy in a Small Open Economy: A Dynamic Stochastic General Equilibrium Approach," MPRA Paper 802, University Library of Munich, Germany.
    20. Grace Lee Ching Yap, 2020. "Optimal Filter Approximations for Latent Long Memory Stochastic Volatility," Computational Economics, Springer;Society for Computational Economics, vol. 56(2), pages 547-568, August.

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:iamo10:90802. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/iamoode.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.