Carbon price returns prediction using a hybrid model
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In the current context of global warming, the prediction of carbon prices has acquired a prominentrole since carbon price constitutes a powerful tool in the operation of artificial carbon markets andthe design of mechanisms oriented to mitigate climate change. A major challenge for carbon priceforecasting is the modeling of non-linear effects in the time series, for which the use of hybridmodels seems to be an appealing alternative to explore. This paper studies the performance of ahybrid model which weights the results from the exponential smoothing model, nonlinearautoregressive neural network, and the autoregressive integrated moving average model. Theseweights are determined by (i) assuming equal weights, (ii) cross validated errors, and (iii) using aneural network to optimize the individual weights. The results confirm the importance ofmodeling the non-linear effects of time series and the capacity of hybrid models in predictingcarbon prices.Page
