An innovative decision-making system integrating multifractal analysis and volatility forecasting

In the global economic system, fluctuations in crude oil prices have a profound impact on investor confidence and international economic development. Due to the complexity and multi-scale nature of the crude oil market, forecasting its fluctuations presents a significant challenge. Addressing this issue, this paper proposes an innovative decision-making system that encompasses two core modules: fractal market analysis and volatility forecasting. In the fractal analysis module, the system employs rolling window technology to enhance detrended fluctuation analysis and partition function methods, tracking the dynamic patterns in the Brent crude oil market. It investigates the auto-correlation and cross-correlation between spot and futures markets and explores the multifractal features of realized volatility constructed from 5-min high-frequency data. In the volatility forecasting module, the system integrates data preprocessing strategies, predictive algorithms, including two traditional econometric methods and four advanced neural network models, optimization mechanisms, and evaluation functions to develop a multi-objective combined forecasting model. This model effectively addresses the challenges of high-frequency data prediction. Experiments demonstrate the short-term predictability of the Brent crude oil market from both intraday and high-frequency data perspectives, revealing that small fluctuations exhibit stronger persistence than large ones and confirming the superiority of the multi-objective combined forecasting model. Thus, the development and application of this system not only enrich the theoretical research on the crude oil market but also provide a valuable decision-making tool for global energy market participants.