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Short-term European Union Allowance price forecasting with artificial neural networks

Agustín García (), Agustín García () and Miguel A. Jaramillo-Morán ()
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Agustín García: University of Extremadura, Spain
Agustín García: Tufts University, United States
Miguel A. Jaramillo-Morán: University of Extremadura, Spain

Authors registered in the RePEc Author Service: Agustin Garcia Garcia ()

Entrepreneurship and Sustainability Issues, 2020, vol. 8, issue 1, 261-275

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 (search for similar items in EconPapers)
JEL-codes: C45 Q50 Q52 (search for similar items in EconPapers)
Date: 2020
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DOI: 10.9770/jesi.2020.8.1(18)

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