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Mean-reversion, non-linearities and the dynamics of industrial metal prices. A forecasting perspective

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  • Rubaszek, Michał
  • Karolak, Zuzanna
  • Kwas, Marek

Abstract

We analyse the dynamics of real prices for main non-ferrous industrial metals: aluminium, copper, nickel and zinc. The estimates based on monthly data from 1980 to 2019 show that the prices are mean reverting and the pace of mean reversion is regime dependent. The results of the out-of-sample forecasting competition provide ample evidence that mean-reverting models deliver significantly better forecasts than the naive random walk. However, allowing for non-linearity by introducing threshold structure does not lead to further improvement in the quality of forecasts.

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  • Rubaszek, Michał & Karolak, Zuzanna & Kwas, Marek, 2020. "Mean-reversion, non-linearities and the dynamics of industrial metal prices. A forecasting perspective," Resources Policy, Elsevier, vol. 65(C).
    • Handle: RePEc:eee:jrpoli:v:65:y:2020:i:c:s0301420719305379
    DOI: 10.1016/j.resourpol.2019.101538
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    1. 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.
    2. Ca’ Zorzi, Michele & Kolasa, Marcin & Rubaszek, Michał, 2017. "Exchange rate forecasting with DSGE models," Journal of International Economics, Elsevier, vol. 107(C), pages 127-146.
    3. Jan J. J. Groen & Paolo A. Pesenti, 2011. "Commodity Prices, Commodity Currencies, and Global Economic Developments," NBER Chapters, in: Commodity Prices and Markets, pages 15-42, National Bureau of Economic Research, Inc.
    4. Joëts, Marc & Mignon, Valérie & Razafindrabe, Tovonony, 2017. "Does the volatility of commodity prices reflect macroeconomic uncertainty?," Energy Economics, Elsevier, vol. 68(C), pages 313-326.
    5. repec:bla:jecsur:v:13:y:1999:i:5:p:551-76 is not listed on IDEAS
    6. Pesaran, M Hashem & Timmermann, Allan, 1992. "A Simple Nonparametric Test of Predictive Performance," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 561-565, October.
    7. Yu-Chin Chen & Kenneth S. Rogoff & Barbara Rossi, 2010. "Can Exchange Rates Forecast Commodity Prices?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 125(3), pages 1145-1194.
    8. Sánchez Lasheras, Fernando & de Cos Juez, Francisco Javier & Suárez Sánchez, Ana & Krzemień, Alicja & Riesgo Fernández, Pedro, 2015. "Forecasting the COMEX copper spot price by means of neural networks and ARIMA models," Resources Policy, Elsevier, vol. 45(C), pages 37-43.
    9. Rossen, Anja, 2015. "What are metal prices like? Co-movement, price cycles and long-run trends," Resources Policy, Elsevier, vol. 45(C), pages 255-276.
    10. Chen, Jinyu & Zhu, Xuehong & Zhong, Meirui, 2019. "Nonlinear effects of financial factors on fluctuations in nonferrous metals prices: A Markov-switching VAR analysis," Resources Policy, Elsevier, vol. 61(C), pages 489-500.
    11. Fernandez, Viviana, 2017. "A historical perspective of the informational content of commodity futures," Resources Policy, Elsevier, vol. 51(C), pages 135-150.
    12. Shu-Ling Chen & John D. Jackson & Hyeongwoo Kim & Pramesti Resiandini, 2014. "What Drives Commodity Prices?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 96(5), pages 1455-1468.
    13. Bruce Hansen, 1999. "Testing for Linearity," Journal of Economic Surveys, Wiley Blackwell, vol. 13(5), pages 551-576, December.
    14. Nguyen, Bao H. & Okimoto, Tatsuyoshi, 2019. "Asymmetric reactions of the US natural gas market and economic activity," Energy Economics, Elsevier, vol. 80(C), pages 86-99.
    15. Ine Van Robays, 2016. "Macroeconomic Uncertainty and Oil Price Volatility," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(5), pages 671-693, October.
    16. Lyócsa, Štefan & Molnár, Peter & Todorova, Neda, 2017. "Volatility forecasting of non-ferrous metal futures: Covariances, covariates or combinations?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 51(C), pages 228-247.
    17. Harris, Richard D.F. & Shen, Jian, 2017. "The intrinsic value of gold: An exchange rate-free price index," Journal of International Money and Finance, Elsevier, vol. 79(C), pages 203-217.
    18. He, Kaijian & Lu, Xingjing & Zou, Yingchao & Keung Lai, Kin, 2015. "Forecasting metal prices with a curvelet based multiscale methodology," Resources Policy, Elsevier, vol. 45(C), pages 144-150.
    19. Lo, Ming Chien & Zivot, Eric, 2001. "Threshold Cointegration And Nonlinear Adjustment To The Law Of One Price," Macroeconomic Dynamics, Cambridge University Press, vol. 5(4), pages 533-576, September.
    20. Xu Gong & Boqiang Lin, 2018. "Structural breaks and volatility forecasting in the copper futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(3), pages 290-339, March.
    21. Baumeister, Christiane & Kilian, Lutz & Lee, Thomas K., 2014. "Are there gains from pooling real-time oil price forecasts?," Energy Economics, Elsevier, vol. 46(S1), pages 33-43.
    22. López-Suárez, Carlos Felipe & Rodríguez-López, José Antonio, 2011. "Nonlinear exchange rate predictability," Journal of International Money and Finance, Elsevier, vol. 30(5), pages 877-895, September.
    23. Gargano, Antonio & Timmermann, Allan, 2014. "Forecasting commodity price indexes using macroeconomic and financial predictors," International Journal of Forecasting, Elsevier, vol. 30(3), pages 825-843.
    24. Joëts, Marc & Mignon, Valérie & Razafindrabe, Tovonony, 2017. "Does the volatility of commodity prices reflect macroeconomic uncertainty?," Energy Economics, Elsevier, vol. 68(C), pages 313-326.
    25. Pincheira Brown, Pablo & Hardy, Nicolás, 2019. "Forecasting base metal prices with the Chilean exchange rate," Resources Policy, Elsevier, vol. 62(C), pages 256-281.
    26. Liu, Chang & Hu, Zhenhua & Li, Yan & Liu, Shaojun, 2017. "Forecasting copper prices by decision tree learning," Resources Policy, Elsevier, vol. 52(C), pages 427-434.
    27. Harvey, David I. & Leybourne, Stephen J. & Whitehouse, Emily J., 2017. "Forecast evaluation tests and negative long-run variance estimates in small samples," International Journal of Forecasting, Elsevier, vol. 33(4), pages 833-847.
    28. Akram, Q. Farooq, 2009. "Commodity prices, interest rates and the dollar," Energy Economics, Elsevier, vol. 31(6), pages 838-851, November.
    29. Buncic, Daniel & Moretto, Carlo, 2015. "Forecasting copper prices with dynamic averaging and selection models," The North American Journal of Economics and Finance, Elsevier, vol. 33(C), pages 1-38.
    30. Hansen,B.E., 1999. "Testing for linearity," Working papers 7, Wisconsin Madison - Social Systems.
    31. Roberts, Mark C., 2009. "Duration and characteristics of metal price cycles," Resources Policy, Elsevier, vol. 34(3), pages 87-102, September.
    32. Li, Gang & Li, Yong, 2015. "Forecasting copper futures volatility under model uncertainty," Resources Policy, Elsevier, vol. 46(P2), pages 167-176.
    33. Chen, Yanhui & He, Kaijian & Zhang, Chuan, 2016. "A novel grey wave forecasting method for predicting metal prices," Resources Policy, Elsevier, vol. 49(C), pages 323-331.
    34. 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.
    35. Wang, Chao & Zhang, Xinyi & Wang, Minggang & Lim, Ming K. & Ghadimi, Pezhman, 2019. "Predictive analytics of the copper spot price by utilizing complex network and artificial neural network techniques," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    36. Laura Coroneo & Fabrizio Iacone, 2015. "Comparing predictive accuracy in small samples," Discussion Papers 15/15, Department of Economics, University of York.
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    More about this item

    Keywords

    Industrial metal prices; Forecasting; Autoregressive models; Threshold models;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q31 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - Demand and Supply; Prices

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