Engineering

Volatility (finance)

Spot contract

Ecology

Biology

Futures contract

Spot market

Telecommunications

Economics

Data mining

Computer science

Signal processing

Greenhouse gas

Autoregressive model

Carbon price

Econometrics

Artificial intelligence

Financial economics

Econometric model

Autoregressive conditional heteroskedasticity

Artificial neural network

Radar

Electricity

Electrical engineering

Journal of Cleaner Production

Carbon spot price forecasting result is important for both policymakers and market participants. However, because of the complex features of carbon spot price, accurate forecasting is very difficult. To achieve a better prediction precision, a hybrid model combined with complete ensemble empirical mode decomposition (CEEMD), co-integration model (CIM), generalized autoregressive conditional heteroskedasticity model (GARCH), and grey neural network (GNN) optimized by ant colony algorithm (ACA) is proposed. Then it is validated by using data collected from European Union emission trading scheme (EU ETS). The results indicate that the performance of the chosen model is remarkably better than that of other models. Therefore, the hybrid model could be used more frequently for carbon spot price forecasting in the future.

A hybrid model using signal processing technology, econometric models and neural network for carbon spot price forecasting