Forecasting European thermal coal spot prices

• Autorzy: Krzemień A. , Riesgo Fernandez P. , Suárez Sánchez A. , Sánchez Lasheras F.

Identyfikatory

DOI

10.1016/j.jsm.2016.04.002

• Języki publikacji: EN

Abstrakty

EN

This paper presents a one-year forecast of European thermal coal spot prices by means of time series analysis, using data from IHS McCloskey NW Europe Steam Coal marker (MCIS). The main purpose was to achieve a good fit for the data using a quick and feasible method and to establish the transformations that better suit this marker, together with an affordable way for its validation. Time series models were selected because the data showed an autocorrelation systematic pattern and also because the number of variables that influence European coal prices is very large, so forecasting coal prices as a dependent variable makes necessary to previously forecast the explanatory variables. A second-order Autoregressive process AR(2) was selected based on the autocorrelation and the partial autocorrelation function. In order to determine if the results obtained are a good fit for the data, the possible drivers that move the European thermal coal spot prices were taken into account, establishing a hypothesis in which they were divided into four categories: (1) energy side drivers, that directly relates coal prices with other energy commodities like oil and natural gas; (2) demand side drivers, that relates coal prices both with the Western World economy and with emerging economies like China, in connection with the demand for electricity in these economies; (3) commodity currency drivers, that have an influence for holders of different commodity currencies in countries that export or import coal; and (4) supply side drivers, involving the production costs, transportation, etc. Finally, in order to analyse the time series model performance a Generalized Regression Neural Network (GRNN) was used and its performance compared against the whole AR(2) process. Empirical results obtained confirmed that there is no statistically significant difference between both methods. The GRNN analysis also allowed pointing out the main drivers that move the European Thermal Coal Spot prices: crude oil, USD/CNY change and supply side drivers.

Słowa kluczowe

EN

thermal coal; price forecasting; time series analysis; neural network; autoregressive model

PL

węgiel energetyczny; prognoza cen; analiza szeregów czasowych; sieć neuronowa; model autoregresyjny

• Wydawca: Elsevier ; Główny Instytut Górnictwa
• Czasopismo: Journal of Sustainable Mining
• Rocznik: 2015
• Tom: Vol. 14, iss. 4
• Strony: 203--210
• Opis fizyczny: Bibliogr. 39 poz.

Twórcy

autor

Krzemień A.

• Central Mining Institute, Plac Gwarków 1, 40-166, Katowice, Poland

autor

Riesgo Fernandez P.

• Oviedo School of Mining, Energy and Materials Engineering, University of Oviedo, Independencia 13, 33004, Oviedo, Spain, tel.: [phone]; fax: [phone]

autor

Suárez Sánchez A.

• Oviedo School of Mining, Energy and Materials Engineering, University of Oviedo, Independencia 13, 33004, Oviedo, Spain, tel.: [phone]; fax: [phone]

autor

Sánchez Lasheras F.

• Department of Construction and Manufacturing Engineering, University of Oviedo, 33204, Gijón, Spain, tel.: [phone]; fax: [phone]

Bibliografia

• 1. Alberola, E., Chevallier, J., & Chèze, B. (2008). Price drivers and structural breaks in European carbon prices 2005-2007. Energy Policy, 36(2), 787-797. http://doi.org/10.1016/j.enpol.2007.10.029.
• 2. Aldrich, J. (1997). R. A. Fisher and the making of maximum likelihood 1912-1922. Statistical Science, 12(3), 162-176. http:// doi.org/10.1214/ss/1030037906.
• 3. Baumeister, C., & Kilian, L. (2012). Real-time forecasts of the real price of oil. Journal of Business & Economic Statistics, 30(2), 326-336. http://doi.org/10.1080/07350015.2011.648859.
• 4. Behmiri, N. B., & Manso, J. R. P. (2013). Crude oil price forecasting techniques: a comprehensive review of literature. Retrieved from https://caia.org/sites/default/files/3.RESEARCHREVIEW.pdf.
• 5. Box, G. E. P., & Jenkins, G. M. (1976). Time series analysis. Forecasting and control. San Francisco: Holden-Day.
• 6. BRG. (2014). Energy study 2014. Reserves, resources and availability of Energy Resources (18). Hannover, Germany. Retrieved from http://www.bgr.bund.de/EN/Themen/Energie/Produkte/energy_study_2014_summary_en.html.
• 7. Clark, T. E., & West, K. D. (2007). Approximately normal tests for equal predictive accuracy in nested models. Journal of Econometrics, 138(1), 291-311. http://doi.org/10.1016/j.jeconom.2006.05.023.
• 8. Crespo Cuaresma, J., Hlouskova, J., Kossmeier, S., & Obersteiner, M. (2004). Forecasting electricity spot-prices using linear univariate time-series models. Applied Energy, 77(1), 87-106. http://dx.doi.org/10.1016/S0306-2619(03)00096-5.
• 9. Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit Root. Journal of the American Statistical Association, 74(366), 427-431. http://doi.org/10.2307/2286348.
• 10. Euracoal. (2015). EURACOAL market report 2/2015. Retrieved from http://euracoal.eu/library/coal-market-reports/.
• 11. European Commission. (2013). Energy challenges and policy. Commission contribution to the European Council of 22 may 2013.
• 12. European Commission. (2014). Communication from the commission to the european parliament, the council, the european economic and social committee and the committee of the regions - A policy framework for climate and energy in the period from 2020 to 2030. COM(2014) 15 final. Brussels.
• 13. Feng, Y. Z., Zhao, H. W., Chen, Y., Tian, L. Q., & Wang, P. (2009). Price forecasting algorithm for coal and electricity based on PSO and RBF neural network. In 2009 IEEE International Conference on Control and Automation, ICCA 2009 (pp. 1365-1369). http://doi.org/10.1109/ICCA.2009.5410509.
• 14. Fernández Benitez, J. A. (2003). La tecnología de la energía en la generación de electricidad. Anexo Técnico III. Centrales de Carbón. Madrid, Spain: Fundación COTEC.
• 15. García-Martos, C., Rodríguez, J., & Sánchez, M. J. (2013). Modelling and forecasting fossil fuels, CO2 and electricity prices and their volatilities. Applied Energy, 101, 363-375. http://doi.org/10.1016/j.apenergy.2012.03.046.
• 16. Gargano, A., & Timmermann, A. (2014). Forecasting commodity price indexes using macroeconomic and financial predictors. International Journal of Forecasting, 30, 825-843. http://dx.doi.org/10.1016/j.ijforecast.2013.09.003.
• 17. Groen, J. J., & Presenti, P. A. (2010). Commodity prices, commodity currencies, and global economic developments. Working Paper No. 15743. Cambridge, Massachusetts, USA: National Bureau of Economic Research http://www.nber.org/papers/w15743.pdf.
• 18. International Energy Agency. (2013). Coal medium-term market report. Retrieved from http://www.iea.org/publications/freepublications/publication/medium-term-coal-marketreport- 2013.html.
• 19. International Energy Agency. (2015a). Energy balances of OECD countries 2015. IEA report.
• 20. International Energy Agency. (2015b). Key coal trends. IEA report.
• 21. Joint Ore Reserves Committee. (2012). The JORC code. The Australasian code for reporting of exploration results. Australia: Mineral Resources and Ore Reserves. Retrieved from http://www.maneyonline.com/doi/abs/10.1179/aes.2001.110.3.121.
• 22. Lilliefors, H. W. (1967). On the Kolmogorov-Smirnov test for normality with mean and variance unknown. Journal of the American Statistical Association, 62(318), 399-402. http://doi.org/10.2307/2283970.
• 23. Lix, L. M., Keselman, J. C., & Keselman, H. J. (1996). Consequences of assumption violations revisited: a quantitative review of alternatives to the one-way analysis of variance F test. Review of Educational Research, 66(4), 579-619. http://doi.org/10.3102/00346543066004579.
• 24. Ljung, G. M., & Box, G. E. (1978). On a measure of lack of fit in time series models. Biometrika, 65(2), 297-303. http://doi.org/10.1093/biomet/65.2.297.
• 25. Masters, T. (1995). Advanced algorithms for neural networks: A C++ sourcebook. New York, USA: John Wiley & Sons, Inc.
• 26. Mohr, S. H., & Evans, G. M. (2009). Forecasting coal production until 2100. Fuel. http://doi.org/10.1016/j.fuel.2009.01.032.
• 27. Owen, A. D. (2006). Renewable energy: externality costs as market barriers. Energy Policy, 34(5), 632-642. http://doi.org/10.1016/j.enpol.2005.11.017.
• 28. Pan-European Reserves and Resources Reporting Committee. (2013). PERC reporting standard. Pan-European standard for reporting of exploration results, mineral resources and reserves. Bruxelles, Belgium.
• 29. Panella, M., Barcellona, F., & D'Ecclesia, R. L. (2012). Forecasting energy commodity prices using neural networks. Advances in Decision Sciences, 2012, 1-26. http://doi.org/10.1155/2012/289810.
• 30. Patzek, T. W., & Croft, G. D. (2010). A global coal production forecast with multi-Hubbert cycle analysis. Energy, 35(8), 3109-3122. http://doi.org/10.1016/j.energy.2010.02.009.
• 31. Sánchez Lasheras, F., de Cos Juez, F. J., Suárez Sánchez, A., Krzemień, A., & Riesgo Fernández, P. (2015). Forecasting the COMEX copper spot price by means of neural networks and ARIMA models. Resources Policy, 45, 37-43. http://doi.org/10.1016/j.resourpol.2015.03.004.
• 32. Sawa, T. (1978). Information criteria for discriminating among alternative regression models. Econometrica: Journal of the Econometric Society, 1273-1291. http://doi.org/10.2307/1913828.
• 33. Specht, D. F. (1990). Probabilistic neural networks. Neural Networks, 3(1), 109-118. http://doi.org/. http://dx.doi.org/10.1016/0893-6080(90)90049-Q.
• 34. Specht, D. F. (1991). A general regression neural network. IEEE Transactions on Neural Networks, 2(6), 568-576. http://doi.org/10.1109/72.97934.
• 35. Suárez Sánchez, A., Krzemień, A., Riesgo Fernández, P., Iglesias Rodríguez, F. J., Sánchez Lasheras, F., & de Cos Juez, F. J. (2015). Investment in new tungsten mining projects. Resources Policy, 46, 177-190. http://doi.org/10.1016/j.resourpol.2015.10.003.
• 36. Sugiura, N. (1978). Further analysis of the data by Akaike's information criterion of model fitting. Suri-Kagaku (Mathematic Science), 153, 12-18. http://dx.doi.org/10.1080/03610927808827599.
• 37. The World Bank. (2015). World Bank commodity price data. Retrieved August 25, 2015, from http://www.worldbank.org/.
• 38. Van Ruijven, B., & van Vuuren, D. P. (2009). Oil and natural gas prices and greenhouse gas emission mitigation. Energy Policy, 37(11), 4797-4808. http://doi.org/10.1016/j.enpol.2009.06.037.
• 39. Weron, R., & Misiorek, A. (2008). Forecasting spot electricity prices: a comparison of parametric and semiparametric time series models. International Journal of Forecasting, 24(4), 744-763. http://doi.org/10.1016/j.ijforecast.2008.08.004.