Analysis of the Dynamic Evolutionary Behavior of American Heating Oil Spot and Futures Price Fluctuation Networks

Chen, Huan; Li, Tian; Wang, Minggang; Zhen, Zaili

doi:10.3390/su9040574

Cited by 12 publications

(11 citation statements)

References 34 publications

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“…Energy and environmental issues arise in a range of important situations, including environmental policies [1,2], CO 2 emissions [3,4], GHG emissions [6,7], wind speed prediction [8], oil prices [9,10], and residential fuel choice [11]. One approach potentially leads to describing just one facet of a more complex issue.…”

Section: Discussionmentioning

confidence: 99%

“…As oil prices receive increasing attention from the public, researchers prefer to study the dynamic behavior of heating oil spot and futures prices, especially for American heating oil spot and futures price fluctuation networks due to America's influence on global markets [9]. Chen et al [9] processed the spot and future pricing of American heating oil using coarse grain processing technology based on the complex network view, and found that the cumulative time of new nodes appearing in either spot or futures price network is not random, but exhibits the growth trend of straight line.…”

Section: Important Issues For China's Energy Economics and Managementmentioning

confidence: 99%

“…Chen et al [9] processed the spot and future pricing of American heating oil using coarse grain processing technology based on the complex network view, and found that the cumulative time of new nodes appearing in either spot or futures price network is not random, but exhibits the growth trend of straight line. Meanwhile, the power law distributions of spot and futures price fluctuations in different periods present regularity and complexity.…”

Section: Important Issues For China's Energy Economics and Managementmentioning

confidence: 99%

“…Moreover, these prices are strongly correlated in periods of stable fluctuation, but are weakly correlated in periods of sharp fluctuation. The study presents the time distribution characteristics of important modes in the networks and the evolution results of the topological properties [9], while in most previous studies of international oil price transmission, the long-run relationship and short-run dynamics of gasoline and diesel price changes in China are tested [10]. It was found that China's gasoline and diesel prices increase quickly when international crude oil prices rise, but decrease slowly when international crude oil prices decline.…”

Section: Important Issues For China's Energy Economics and Managementmentioning

confidence: 99%

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A Review of Low-Carbon Transformation and Energy Innovation Issues in China

2017

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Scale-oriented economic development in China has given rise to problems associated with high energy consumption and severe environmental pollution. Thus, the 7th China Annual Conference of Energy Economics and Management provides a platform for presenting ongoing research activities in order to exchange research ideas in the area of low-carbon economics and sustainable development for China. We thank Sustainability for providing this timely special issue. This editorial highlights the contents and methodologies of this conference special issue, presenting several important issues in energy economics and management.

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Section: Discussionmentioning

confidence: 99%

Section: Important Issues For China's Energy Economics and Managementmentioning

confidence: 99%

Section: Important Issues For China's Energy Economics and Managementmentioning

confidence: 99%

Section: Important Issues For China's Energy Economics and Managementmentioning

confidence: 99%

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A Review of Low-Carbon Transformation and Energy Innovation Issues in China

2017

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“…First of all, the coarse graining algorithm [39] is taken to process these data, and the futures price series of heating oil are denoted by P f ( t )( t = 1, 2, 3,…, n , n = 3765). It is assumed that P f ( t ) represents the current price, and P f ( t − 1) refers to the price of the day before t , then the sequence of futures price volatility is Δ P f ( t ) = P f ( t ) − P f ( t − 1).…”

Section: Methodsmentioning

confidence: 99%

Research on the forward-looking behavior judgment of heating oil price evolution based on complex networks

2018

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Analyzing and predicting the trend of price fluctuation has been receiving more and more attention, as price risk has become the focus of risk control research in heating oil futures market. A novel time series prediction model combined with the complex network method is put forward in the paper. First of all, this paper counts the cumulative time interval of different nodes in the network, and fits its growth trend with the Fourier model. Then a novel price fluctuation prediction model is established based on the effective information such as some topology properties extracted from the network. The results show that the Fourier model can predict the emergence time of new nodes in the next stage, and the established price fluctuation prediction model can infer the names of nodes in the prediction interval, so as to determine the forward-looking behavior of price evolution. Besides, liken to the NAR neural network, the prediction results obtained by the proposed method also show superiority, which has important theoretical value and academic significance for early warning and prediction of price behavior in the heating oil futures market.

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