Title
Short-term European Union Allowance price forecasting with artificial neural networks
Abstract
The European Union Emissions Trading Scheme (EU ETS) was created to reduce greenhouse gas emissions. Companies producing carbon emissions have to manage associated cash flows by buying or selling carbon allowances. Moreover, future carbon prices could affect company decision making on decarbonization technology investments. In this paper, we forecasted short-term future carbon allowance prices using an artificial intelligence tool: a neural network. The resulting mean error was 1.7617 %. This is indicative of very good performance for a time series whose evolution is influenced by subjective economic and political decisions. The inclusion in the forecasting model of variables possibly directly related to the evolution of the price of CO2 emission allowances did not improve prediction accuracy. Therefore, we can assume that emission allowances evolve following a random path. The neural network provided reliable predictions which agents selling or buying allowances can use to make their decisions.
Keywords
European Union Allowances (EUA), carbon allowance price, neural networks, time series forecasting
JEL classifications
Q50 , Q52 , C45
URI
http://jssidoi.org/jesi/article/618
DOI
Pages
261-275
Funding
This research was funded by Junta de Extremadura (Extremadura Regional Government) Research Group Support Program Grant GR18075 (co-financed by European Union FEDER funds)This is an open access issue and all published articles are licensed under a
Creative Commons Attribution 4.0 International License
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