Forecasts of China Mainland New Energy Index Prices through Gaussian Process Regressions

Abstract

Energy index price forecasting has long been a crucial undertaking for investors and regulators. This study examines the daily price predicting problem for the new energy index on the Chinese mainland market from January 4, 2016 to December 31, 2020 as insufficient attention has been paid to price forecasting in the literature for this crucial financial metric. Gaussian process regressions facilitate our analysis, and training procedures of the models make use of cross-validation and Bayesian optimizations. From January 2, 2020 to December 31, 2020, the price was properly projected by the created models, with an out-of-sample relative root mean square error of 1.8837%. The developed models may be utilized in investors’ and policymakers’ policy analysis and decision-making processes. Because the forecasting results provide reference information about price patterns indicated by the models, they may also be useful in building of similar energy indices.