Forecasting European Union allowances futures: The role of technical indicators

With the aim of studying the prognostic power of monthly European Carbon futures prices, we employ a wide variety of potential forecasting indicators, including economic variables like uncertainty indicators, energy prices, and stock index data, in addition to exploring the influence of technical indicators on forecasting for the first time. In both in-sample and out-of-sample results, technical indicators display statistically significant ability, exceeding that of economic variables. To gain valuable insight into which indicators are comparatively relevant to the price of EUAs, three types of forecasting methods: univariate predictive regression, dimensionality reduction techniques, and ensemble learning algorithms are applied. Results show that the first diffusion index in the principal component regression and partial least squares model is greatly influenced by the changes of technical indicators, while only having a weak association with economic variables. With respect to ensemble learning algorithms like Xgboost, greater importance is given to technical indicators compared to economic variables when making predictions. Furthermore, technical indicators demonstrate stable economic value to investors in the form of positive CER gains. Our findings present novel evidence for the predictive capacity of technical indicators and can assist policymakers in formulating sustainable and effective energy policies. •Advanced ensemble learning algorithms including Xgboost and Lightgbm are employed.•A thorough analysis of the loading and importance of a single feature is conducted.•Technical indicators show notable significance, exceeding that of economic variables.•Technical indicators exhibit stable economic value in terms of positive CER gains.