Predicting Oil Prices: An Analysis of Oil Price Volatility Cycle and Financial Markets

Abstract

ABSTRACT Given the importance of crude oil prices in the world economy, accurate price prediction has drawn extensive attention. Nevertheless, because of the complexity of the crude oil market, most traditional forecasting algorithms fail to meet the accuracy requirements. To achieve higher precision, this paper proposes a novel hybrid model for crude oil price forecasting by combining a Hodrick-Prescott filter with X12 methods and adjusting the order used. Application of our model on both West Texas Intermediate and Brent oil prices forecasting demonstrates its accuracy. The results of various forecasting performance evaluation criteria indicate that the model has stronger stability and better accuracy. The mechanism of seasonal and periodic factors is also analyzed, which provides remarkable references to other time-series predictions. Establishing two different types of predictive models that combine multiple knowledge effectively has obvious advantages over other models and provides more reliable cutting-edge information for designing a Chinese energy development strategy.