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An improved wavelet–ARIMA approach for forecasting metal prices

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  • Kriechbaumer, Thomas
  • Angus, Andrew
  • Parsons, David
  • Rivas Casado, Monica

Abstract

Metal price forecasts support estimates of future profits from metal exploration and mining and inform purchasing, selling and other day-to-day activities in the metals industry. Past research has shown that cyclical behaviour is a dominant characteristic of metal prices. Wavelet analysis enables to capture this cyclicality by decomposing a time series into its frequency and time domain. This study assesses the usefulness of an improved combined wavelet-autoregressive integrated moving average (ARIMA) approach for forecasting monthly prices of aluminium, copper, lead and zinc. The performance of ARIMA models in forecasting metal prices is demonstrated to be increased substantially through a wavelet-based multiresolution analysis (MRA) prior to ARIMA model fitting. The approach demonstrated in this paper is novel because it identifies the optimal combination of the wavelet transform type, wavelet function and the number of decomposition levels used in the MRA and thereby increases the forecast accuracy significantly. The results showed that, on average, the proposed framework has the potential to increase the accuracy of one month ahead forecasts by $53/t for aluminium, $126/t for copper, $50/t for lead and $51/t for zinc, relative to classic ARIMA models. This highlights the importance of taking into account cyclicality when forecasting metal prices.

Suggested Citation

  • Kriechbaumer, Thomas & Angus, Andrew & Parsons, David & Rivas Casado, Monica, 2014. "An improved wavelet–ARIMA approach for forecasting metal prices," Resources Policy, Elsevier, vol. 39(C), pages 32-41.
    • Handle: RePEc:eee:jrpoli:v:39:y:2014:i:c:p:32-41
    DOI: 10.1016/j.resourpol.2013.10.005
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    as
    1. Helmut Lütkepohl & Fang Xu, 2012. "The role of the log transformation in forecasting economic variables," Empirical Economics, Springer, vol. 42(3), pages 619-638, June.
    2. 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).
    3. Roberts, Mark C., 2009. "Duration and characteristics of metal price cycles," Resources Policy, Elsevier, vol. 34(3), pages 87-102, September.
    4. Dooley, Gillian & Lenihan, Helena, 2005. "An assessment of time series methods in metal price forecasting," Resources Policy, Elsevier, vol. 30(3), pages 208-217, September.
    5. Luís Aguiar-Conraria & Maria Soares, 2011. "Oil and the macroeconomy: using wavelets to analyze old issues," Empirical Economics, Springer, vol. 40(3), pages 645-655, May.
    6. Davidson, Russell & Labys, Walter C & Lesourd, Jean-Baptiste, 1998. "Wavelet Analysis of Commodity Price Behavior," Computational Economics, Springer;Society for Computational Economics, vol. 11(1-2), pages 103-128, April.
    7. Jammazi, Rania & Aloui, Chaker, 2012. "Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling," Energy Economics, Elsevier, vol. 34(3), pages 828-841.
    8. Jammazi, Rania & Aloui, Chaker, 2010. "Wavelet decomposition and regime shifts: Assessing the effects of crude oil shocks on stock market returns," Energy Policy, Elsevier, vol. 38(3), pages 1415-1435, March.
    9. Yousefi, Shahriar & Weinreich, Ilona & Reinarz, Dominik, 2005. "Wavelet-based prediction of oil prices," Chaos, Solitons & Fractals, Elsevier, vol. 25(2), pages 265-275.
    10. Tonn, Victor Lux & Li, H.C. & McCarthy, Joseph, 2010. "Wavelet domain correlation between the futures prices of natural gas and oil," The Quarterly Review of Economics and Finance, Elsevier, vol. 50(4), pages 408-414, November.
    11. Patrick M. Crowley, 2007. "A Guide To Wavelets For Economists," Journal of Economic Surveys, Wiley Blackwell, vol. 21(2), pages 207-267, April.
    12. Deaton, Angus & Miller, Ron, 1996. "International Commodity Prices, Macroeconomic Performance and Politics in Sub-Saharan Africa," Journal of African Economies, Centre for the Study of African Economies, vol. 5(3), pages 99-191, October.
    13. 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.
    14. Radetzki,Marian, 2008. "A Handbook of Primary Commodities in the Global Economy," Cambridge Books, Cambridge University Press, number 9780521880206, Junio.
    15. Davutyan, Nurhan & Roberts, Mark C., 1994. "Cyclicality in metal prices," Resources Policy, Elsevier, vol. 20(1), pages 49-57, March.
    16. Naccache, Théo, 2011. "Oil price cycles and wavelets," Energy Economics, Elsevier, vol. 33(2), pages 338-352, March.
    17. Catalão, J.P.S. & Pousinho, H.M.I. & Mendes, V.M.F., 2011. "Short-term wind power forecasting in Portugal by neural networks and wavelet transform," Renewable Energy, Elsevier, vol. 36(4), pages 1245-1251.
    18. Labys, W C & Lesourd, J B & Badillo, D, 1998. "The existence of metal price cycles," Resources Policy, Elsevier, vol. 24(3), pages 147-155, September.
    19. Angus Deaton, 1999. "Commodity Prices and Growth in Africa," Journal of Economic Perspectives, American Economic Association, vol. 13(3), pages 23-40, Summer.
    20. Nowotarski, Jakub & Tomczyk, Jakub & Weron, Rafał, 2013. "Robust estimation and forecasting of the long-term seasonal component of electricity spot prices," Energy Economics, Elsevier, vol. 39(C), pages 13-27.
    21. Clinton Watkins & Michael McAleer, 2004. "Econometric modelling of non‐ferrous metal prices," Journal of Economic Surveys, Wiley Blackwell, vol. 18(5), pages 651-701, December.
    22. Nguyen, Hang T. & Nabney, Ian T., 2010. "Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models," Energy, Elsevier, vol. 35(9), pages 3674-3685.
    23. Fernandez, Viviana, 2007. "Wavelet- and SVM-based forecasts: An analysis of the U.S. metal and materials manufacturing industry," Resources Policy, Elsevier, vol. 32(1-2), pages 80-89.
    24. Cashin, Paul & McDermott, C. John & Scott, Alasdair, 2002. "Booms and slumps in world commodity prices," Journal of Development Economics, Elsevier, vol. 69(1), pages 277-296, October.
    25. Schlüter, Stephan & Deuschle, Carola, 2010. "Using wavelets for time series forecasting: Does it pay off?," FAU Discussion Papers in Economics 04/2010, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    26. Viviana Fernandez, 2008. "Traditional versus novel forecasting techniques: how much do we gain?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(7), pages 637-648.
    27. Dehn, Jan, 2000. "The effects on growth of commodity price uncertainty and shocks," Policy Research Working Paper Series 2455, The World Bank.
    28. Tan, Zhongfu & Zhang, Jinliang & Wang, Jianhui & Xu, Jun, 2010. "Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models," Applied Energy, Elsevier, vol. 87(11), pages 3606-3610, November.
    29. 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.
    30. Connor Jeff & Rossiter Rosemary, 2005. "Wavelet Transforms and Commodity Prices," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(1), pages 1-22, March.
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