How uncertain are oil prices? bayesian regularized distribution neural network for forecasting oil prices

Predicting crude oil price volatility presents a significant challenge due to its complexity and sensitivity to various global factors. To address this, this study proposes a framework based on Bayesian Regularized Distributed Neural Networks (BRDNN), integrating econometric probability distribution models with deep learning. Using a Gaussian mixture distribution, the model effectively captures the uncertainty and volatility of oil prices while minimizing cumulative errors and computational complexity. Empirical analysis of Brent and WTI crude oil markets demonstrates that the proposed method outperforms traditional probability distribution models in terms of predictive accuracy and robustness. This research provides a novel approach for quantifying oil price uncertainty, offering valuable insights for energy policy development and risk management strategies.