Open AccessForecasting European thermal coal spot prices
Author(s) -
Alicja Krzemień,
Pedro Riesgo Fernández,
Álvaro Bueno Sánchez,
Fernando Sánchez Lasheras
Publication year - 2015
Publication title -
journal of sustainable mining
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.623
H-Index - 17
eISSN - 2543-4950
pISSN - 2300-3960
DOI - 10.1016/j.jsm.2016.04.002
Subject(s) - coal , commodity , spot contract , economics , autocorrelation , econometrics , order (exchange) , autoregressive model , economy , electricity , currency , consumption (sociology) , partial autocorrelation function , time series , financial economics , macroeconomics , engineering , autoregressive integrated moving average , statistics , futures contract , mathematics , market economy , waste management , finance , social science , electrical engineering , sociology
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