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Multiscale Agricultural Commodities Forecasting Using Wavelet-SARIMA Process

Author

Listed:
  • Mamadou-Diéne Diop

    (Université de Montpellier)

  • Jules Sadefo Kamdem

    (Université de Montpellier
    MRE UR MRE UR 209, (Université de Montpellier), Faculté d’Economie Avenue Raymond Dugrand)

Abstract

Forecasts of spot or future prices for agricultural commodities make it possible to anticipate the favorable or above all unfavorable development of future profits from the exploitation of agricultural farms or agri-food enterprises. Previous research has shown that cyclical behavior is a dominant feature of the time series of prices of certain agricultural commodities, which may be affected by a seasonal component. Wavelet analysis makes it possible to capture this cyclicity by decomposing a time series into its frequency and time domains. This paper proposes a time-frequency decomposition based approach to choose a seasonal auto-regressive aggregate (SARIMA) model for forecasting the monthly prices of certain agricultural futures prices. The originality of the proposed approach is due to the identification of the optimal combination of the wavelet transformation type, the wavelet function and the number of decomposition levels used in the multi-resolution approach (MRA), that significantly increase the accuracy of the forecast. Our SARIMA hybrid approach contributes to take into account the cyclicity and of the seasonality when predicting commodity prices. As a relevant result, our study allows an economic agent, according to his forecasting horizon, to choose according to the available data, a specific SARIMA process for forecasting.

Suggested Citation

  • Mamadou-Diéne Diop & Jules Sadefo Kamdem, 2023. "Multiscale Agricultural Commodities Forecasting Using Wavelet-SARIMA Process," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 21(1), pages 1-40, March.
    • Handle: RePEc:spr:jqecon:v:21:y:2023:i:1:d:10.1007_s40953-022-00329-4
    DOI: 10.1007/s40953-022-00329-4
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    1. Osborn, Denise R, et al, 1988. "Seasonality and the Order of Integration for Consumption," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 50(4), pages 361-377, November.
    2. Phillips, P C B, 1987. "Time Series Regression with a Unit Root," Econometrica, Econometric Society, vol. 55(2), pages 277-301, March.
    3. Levhari, David & Levy, Haim, 1977. "The Capital Asset Pricing Model and the Investment Horizon," The Review of Economics and Statistics, MIT Press, vol. 59(1), pages 92-104, February.
    4. Víctor Gómez & Agustín Maravall, 1998. "Seasonal Adjustment and Signal Extraction in Economic Time Series," Working Papers 9809, Banco de España.
    5. Guy Melard & Jean-Michel Pasteels, 2000. "Automatic ARIMA modeling including interventions, using time series expert software," ULB Institutional Repository 2013/13744, ULB -- Universite Libre de Bruxelles.
    6. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    7. repec:bla:jfinan:v:53:y:1998:i:2:p:549-573 is not listed on IDEAS
    8. Hayat, Aziz & Bhatti, M. Ishaq, 2013. "Masking of volatility by seasonal adjustment methods," Economic Modelling, Elsevier, vol. 33(C), pages 676-688.
    9. Melard, G. & Pasteels, J. -M., 2000. "Automatic ARIMA modeling including interventions, using time series expert software," International Journal of Forecasting, Elsevier, vol. 16(4), pages 497-508.
    10. M. Vannucci & F. Corradi, 1999. "Covariance structure of wavelet coefficients: theory and models in a Bayesian perspective," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(4), pages 971-986.
    11. Gencay, Ramazan & Selcuk, Faruk & Whitcher, Brandon, 2005. "Multiscale systematic risk," Journal of International Money and Finance, Elsevier, vol. 24(1), pages 55-70, February.
    12. Gençay, Ramazan & Gençay, Ramazan & Selçuk, Faruk & Whitcher, Brandon J., 2001. "An Introduction to Wavelets and Other Filtering Methods in Finance and Economics," Elsevier Monographs, Elsevier, edition 1, number 9780122796708.
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