The crude oil is of dual attributes, strategic and financial. Oil price fluctuations attract attention around the world, especially the oil price fluctuation change point is considered more important for energy finance industry. Based on this idea, an exploratory research direction is introduced in this paper. Monthly international crude oil prices were taken as the study objective and the PPM-KM integration model was established by extending product partition model (PPM) to adapt to measure, cluster, and identify the posterior probability of change points of international crude oil price. First, this paper measured the mutations posteriori probability of the oil price based on the PPM model in order to distinguish and analyze the mutations of the oil price, and gave the result of the tolerance threshold and mutations of commodity price combined with K-Means clustering method. Second, the Poisson distribution, power-law distribution, and logarithmic-normal distribution were used to build statistical inference model to the catastrophes description, and then corresponding probability distribution functions for simulation and analyses of the monthly crude oil price change point trends were constructed. The results showed that there were 37 significant breaking points in the period of 1986 to 2015. At different time points, the imbalanced structure of market supply and demand, unexpected events, the dollar index, the global geopolitical economic development situation the main oil mutations as the main cause of crude oil price fluctuation respectively. By fitting the distribution of the time interval of change points, this paper preliminary think the time interval of monthly international crude oil price change points obeys power-law distribution assumption is reasonable.
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