Abstract
According to the fluctuation series of the conventional gasoline spot prices (CGSP) in New York Harbor (NYH) and U.S. Gulf Coast (GC), this paper defines the fluctuation modes by the coarse-grained method based on the CGSP series in the two harbors. The fluctuation series are converted into the characters by means of the sliding window, where five symbol series is used as a fluctuation mode, one day was used as a step to slide in the data window, and the conventional gasoline spot prices fluctuation network (CGSPFN) is constructed in the two harbors. Then the evolutionary rule of the new nodes in the CGSPFN is analyzed, such as the strength and distribution, average shortest paths, conversion cycle, betweenness, and clustering coefficient of the nodes are calculated in different periods. The result indicates that the cumulative time of the new nodes which appeared in the CGSPFN is not random but presents a high linear growth trend, which reveals the linear features of the cumulative time of abnormal points when the gasoline price fluctuation appears. The betweenness and clustering coefficient shows that the nodes with the larger strength have smaller betweenness and clustering coefficients, the nodes with the larger betweenness have smaller strength and clustering coefficients, and the nodes with the larger clustering coefficients have smaller betweenness and strength. Meanwhile, the gasoline prices are in a transitional period when the larger indicators appear and have a rising trend, and identifying the transitional period will help the decision maker to grasp the regularity of the changes of the gasoline prices.
Highlights
Gasoline is one of the most consumed petroleum products and an important fuel for engines, which is closely related to the transportation industry [1,2]
As for the SFP and FFP, the power law indexes of the corresponding period in Gulf Coast (GC) are larger than that in New York Harbor (NYH), which illustrate that the strength of the power law distribution about the conventional gasoline spot prices fluctuation network (CGSPFN) in GC is higher in the corresponding period and the power law distribution shows a part of complexity and regularity
Based on the above analysis, it can be seen that the node of the CGSPFN in NYH and GC has a smaller clustering coefficient, average path length, and betweenness, which is different from the random network and chaotic network
Summary
Gasoline is one of the most consumed petroleum products and an important fuel for engines, which is closely related to the transportation industry [1,2]. According to the U.S Energy Information Administration, slower traffic growth has been mirrored in flattening gasoline consumption, retail gasoline prices are up by more than 55% from their cyclical low in February 2016, and the average daily demand for automotive gasoline is 9.201 million barrels in March 2019, which is 1.5% lower than the same period last year These factors have an impact on the gasoline prices. Wang [33] presented the phase space coarse- graining algorithm, which converts a time series into a directed and weighted complex network, and researches the fluctuation behavior of the crude oil and gasoline price based on these methods [34].
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