Analysis and Bayes statistical probability inference of crude oil price change point

Abstract

This paper introduces the Poisson distribution, power-law distribution, and logarithmic-normal distribution as the prior distributions to construct Bayes statistical probability inference model for the simulation of the monthly crude oil price change point trends. Based on basic statistical cognition and product partition model (PPM), the historical change points are defined, identified, and analyzed. The PPM-KM integration model is established by combining PPM model and K-means method to measure, cluster, and identify the posterior probability of change points. The appearance probabilities of change points under different scenarios are calculated and compared for single recursive probabilistic predictions. The results showed there were 37 significant change points during 1986–2015. In different time points, unbalance of market supply-demand structure, sudden geopolitical event, the US dollar index, and global economic development situation have become the main reasons for oil price catastrophes. The monthly crude oil price change point complied with the power-law distribution hypothesis. It provides a new analytical perspective and is beneficial to governments, enterprises and investors to understand the market trends, avoid investment risks and make the right investment decisions.