Applying Artificial Neural Networks to Forecast European Union Allowance Prices: The Effect of Information from Pollutant-Related Sectors

Abstract

In this paper, we forecast the price of CO2 emission allowances using an artificial intelligence tool: neural networks. We were able to provide confident predictions of several future prices by processing a set of past data. Different model structures were tested. The influence of subjective economic and political decisions on price evolution leads to complex behavior that is hard to forecast. We analyzed correlations with different economic variables related to the price of CO2 emission allowances and found the behavior of two to be similar: electricity prices and iron and steel prices. They, along with CO2 emission allowance prices, were included in the forecasting model in order to verify whether or not this improved forecasting accuracy. Only slight improvements were observed, which proved to be more significant when their respective time series trends or fluctuations were used instead of the original time series. These results show that there is some sort of link between the three variables, suggesting that the price of CO2 emission allowances is closely related to the time evolution of the price of electricity and that of iron and steel, which are very pollutant industrial sectors. This can be regarded as evidence that the CO2 market is working properly.

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

“…This result contradicts the findings of Feng et al (2011), who used data from the first phase of the market, suggesting that, once allowances began to be allocated by auctioning, market operation changed. In contrast to Jaramillo and García (2019), the model proposed in this paper provides short-term forecasting prices, that is, it considers the allowances as financial assets, whose time evolution is not affected by the inclusion of additional variables apart from allowance price. Price evolution is compatible with an optimal temporal distribution of allowances.…”

“…Indeed, several authors have pointed out the possible influence of energy and fuel prices on the evolution of CO2 allowance prices (Paolella and Taschini, 2008;Alberola et al, 2009;Keppler and Mansanet-Bataller, 2010;Hammoudeh et al, 2014;Hammoudeh et al, 2015;Convery and Redmond, 2007;Boersen and Scholtens, 2014). In Jaramillo and García (2019), the influence of energy and raw material prices on this variable was studied by analyzing their correlation. It was found that only electricity and iron and steel prices were correlated with the price of CO2 allowances.…”

“…We test the influence of the use of exogenous variables on forecasting accuracy. Taking into account the results reported in Jaramillo and García (2019), only the electricity and iron and steel price time series are considered here. The prices of these three variables were taken from the Thomson Reuters Datastream and Bloomberg Databases.…”

“…We believe that the explanation necessarily depends on the time range of the price predictions and the ability of companies to make decisions that affect their production in the medium or long term. Jaramillo and García (2019) found that allowance prices from EU ETS auctioning phases are related to pollutant sector information in the midterm (the time horizon that is expected to affect company production or investment decisions). Nevertheless, companies do not have the chance to adapt production to a change in fuel prices or to a new technical situation from day to day.…”

Improving Artificial Intelligence Forecasting Models Performance with Data Preprocessing: European Union Allowance Prices Case Study

“…In this way two series are obtained: one describing the global trend of data and the other their seasonal and cyclic oscillations. It has provided good results when applied to electric consumption forecasting [20] and also to EUA prices prediction [33]. Another approximation to this decomposition is a regression algorithm that samples a dataset at different frequencies (MIDAS), which have been developed to deal with econometric series, and have been also applied to carbon prices forecasting [25].…”