Research on patterns in the fluctuation of the co-movement between crude oil futures and spot prices: A complex network approach

The price of crude oil is fluctuating. Researchers focus on the fluctuation of crude oil prices or relationship between crude oil futures and spot prices. However, the relationship also presents fluctuation which draws our attention. This paper designed a complex network approach for examining the dynamics of the co-movement between crude oil futures and spot prices. We defined the co-movement modes by a coarse-graining procedure and analyzed the transformation characteristics between the modes by weighted complex network models and evolutionary models. We analyzed the parameters of these models by using the West Texas Intermediate crude oil future prices and the Daqing (China) crude oil spot prices from November 25, 2002 to March 22, 2011 as sample data. The results indicate that the co-movement modes of the crude oil futures and spot prices are clustered around a few critical modes during the evolution. The co-movement of the crude oil prices has the characteristic of grouping, and the conversion of the co-movement modes requires an average of 5–7days. There are some important transitional phases in the evolution of prices, and the results validate the current trend of rising oil prices. This research may provide information for the oil price decision-making process, and more importantly, provides a new approach for examining the co-movement between variables.

[1]  Aytürk Keles,et al.  The adaptive neuro-fuzzy model for forecasting the domestic debt , 2008, Knowl. Based Syst..

[2]  James Davidson,et al.  Econometric Modelling of the Aggregate Time-Series Relationship Between Consumers' Expenditure and Income in the United Kingdom , 1978 .

[3]  Robert K. Kaufmann,et al.  Oil prices, speculation, and fundamentals: Interpreting causal relations among spot and futures prices , 2009 .

[4]  Zhi Rong,et al.  An approach to research the topology of Chinese temperature sequence based on complex network , 2008 .

[5]  Thomas A. Knetsch,et al.  Forecasting the Price of Crude Oil Via Convenience Yield Predictions , 2007, SSRN Electronic Journal.

[6]  R. Smyth,et al.  Cointegration between oil spot and future prices of the same and different grades in the presence of structural change , 2008, Energy Policy.

[7]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[8]  Yalin Lei,et al.  Economic and social effects analysis of mineral development in China and policy implications , 2013 .

[9]  高湘昀,et al.  Research on fluctuation of bivariate correlation of time series based on complex networks theory , 2012 .

[10]  A. Barabasi,et al.  Weighted evolving networks. , 2001, Physical review letters.

[11]  J. Kurths,et al.  A Comparative Classification of Complexity Measures , 1994 .

[12]  Haizhong An,et al.  The role of fluctuating modes of autocorrelation in crude oil prices , 2014 .

[13]  Yue-Jun Zhang,et al.  Investigating the price discovery and risk transfer functions in the crude oil and gasoline futures markets: Some empirical evidence , 2013 .

[14]  Zhang Li,et al.  Research on the evolution process of virtual community networks , 2008 .

[15]  R. Smyth,et al.  Unit root properties of crude oil spot and futures prices , 2008, Energy Policy.

[16]  Yue-Jun Zhang,et al.  Speculative trading and WTI crude oil futures price movement: An empirical analysis , 2013 .

[17]  Marco A. Janssen,et al.  An adaptive agent model for analysing co-evolution of management and policies in a complex rangeland system , 2000 .

[18]  Jianfeng Guo,et al.  How Does Market Concern Derived from the Internet Affect Oil Prices? , 2013 .

[19]  高湘昀,et al.  Correlation of the holding behaviour of the holding-based network of Chinese fund management companies based on the node top ological characteristics , 2014 .

[20]  Ying Fan,et al.  How Does Oil Price Volatility Affect Non-Energy Commodity Markets? , 2012 .

[21]  Lei Wang,et al.  Oil Risk in Oil Stocks , 2008 .

[22]  Yen-Hsien Lee,et al.  Jump dynamics with structural breaks for crude oil prices , 2010 .

[23]  Andrea Coppola,et al.  Forecasting oil price movements: Exploiting the information in the futures market , 2008 .

[24]  Haizhong An,et al.  Features of the Correlation Structure of Price Indices , 2013, PloS one.

[25]  L. Freeman Centrality in social networks conceptual clarification , 1978 .

[26]  Chin W. Yang,et al.  The dynamics of a nonlinear relationship between crude oil spot and futures prices: A multivariate threshold regression approach , 2009 .

[27]  Investigating price clustering in the oil futures market , 2011 .

[28]  M. Newman,et al.  Renormalization Group Analysis of the Small-World Network Model , 1999, cond-mat/9903357.

[29]  C. Granger,et al.  Co-integration and error correction: representation, estimation and testing , 1987 .

[30]  Huajiao Li,et al.  On the topological properties of the cross-shareholding networks of listed companies in China: Taking shareholders’ cross-shareholding relationships into account , 2014 .

[31]  Yalin Lei,et al.  Design and impact estimation of a reform program of China’s tax and fee policies for low-grade oil and gas resources , 2011 .

[32]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[33]  An Haizhong,et al.  Analysis on the topological properties of the linkage complex network between crude oil future price and spot price , 2011 .