Abstract
Predicting CO2 emission prices is an important and challenging task for policy makers and market participants, as carbon prices follow a stochastic process of complex time series with nonstationary and nonlinear characteristics. Existing literature has focused on highly precise point forecasting, but it cannot correctly solve the uncertainties related to carbon price datasets in most cases. This study aims to develop a hybrid forecasting model to estimate in advance the maximum or minimum loss in the stochastic process of CO2 emission trading price fluctuation. This model can granulate raw data into fuzzy-information granular components with minimum (Low), average (R), and maximum (Up) values as changing space-description parameters. Furthermore, it can forecast carbon prices’ changing space with Low, R, and Up as inputs to support a vector regression. This method’s feasibility and effectiveness is examined using empirical experiments on European Union allowances’ spot and futures prices under the European Union’s Emissions Trading Scheme. The proposed FIG-SVM model exhibits fewer errors and superior performance than ARIMA, ARFIMA, and Markov-switching methods. This study provides several important implications for investors and risk managers involved in trading carbon financial products.
Highlights
Since the launch of the European Union’s Emissions Trading Scheme (EU ETS), carbon dioxide (CO2) emission certificates have been cultivated as a scarce resource
Irregular and unexpected price fluctuations have increased the risk in the carbon market, which affects participants’ confidence and emission-mitigation targets [1]. erefore, it is essential to understand carbon prices’ dynamics and establish a scientific carbon price-forecasting model, as predicting carbon prices is useful for reducing market risk [2]. is study aims to predict EU allowances (EUAs) prices in an effort to assist entities regulated under the EU ETS in managing risk and benefit policy makers concerned with this pricing mechanism
Following their study of fuzzy support vector machine (SVM), we develop a novel Fuzzy Information Granulation (FIG)-SVM model for nonstationary and nonlinear time series prediction. is model can decompose large-scale complex time-series issues into some simpler problems to be handled independently. e original data are firstly extracted with partition sets in order to train and test
Summary
Since the launch of the European Union’s Emissions Trading Scheme (EU ETS), carbon dioxide (CO2) emission certificates have been cultivated as a scarce resource. Irregular and unexpected price fluctuations have increased the risk in the carbon market, which affects participants’ confidence and emission-mitigation targets [1]. Erefore, it is essential to understand carbon prices’ dynamics and establish a scientific carbon price-forecasting model, as predicting carbon prices is useful for reducing market risk [2]. Is study aims to predict EUA prices in an effort to assist entities regulated under the EU ETS in managing risk and benefit policy makers concerned with this pricing mechanism. A scientific prediction tool in the investment field can help speculators seek short-term arbitrage opportunities from volatility trades and develop efficient investment strategies in the carbon market. Given the significance of scientific prediction models, many studies have presented several methods to forecast carbon prices and this literature is commonly divided into two strands [3]. The literature forecasts carbon prices in the context of classic statistical methods, including the ARIMA, GARCH, Mathematical Problems in Engineering
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