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The role of fluctuating modes of autocorrelation in crude oil prices

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  • An, Haizhong
  • Gao, Xiangyun
  • Fang, Wei
  • Huang, Xuan
  • Ding, Yinghui

Abstract

Autocorrelation exists in the crude oil price due to price inertia, the cobweb theorem, model errors, etc. Many researchers have studied the fluctuation of the crude oil price, but few have focused on the autocorrelation fluctuation in crude oil prices. Exploring the fluctuating rules of autocorrelation can aid in understanding the fluctuating mechanism of crude oil prices. To study the role of fluctuating modes of autocorrelation in crude oil prices, which have time series characteristics, this study selected international crude oil spot prices as sample data to employ the methods of statistical physics. The fluctuating modes of autocorrelation were defined by the autocorrelation coefficient, symbolization, and a coarse-graining process. We set the modes as nodes and the transformation between modes as edges; the fluctuating mode weight network of autocorrelation was then built. Thus, the study of autocorrelation fluctuation was transformed to a network study. Then, certain aspects, such as the statistical properties, the “small-world” behavior, and the transmission medium in the network, could be analyzed using complex network theory and analytical methods. The periodicity of the fluctuation was calculated using a spectral analysis method. This study not only describes the fluctuation of the time series more precisely than other methods but also provides ideas for methods of studying the fluctuation of univariate autocorrelations.

Suggested Citation

  • An, Haizhong & Gao, Xiangyun & Fang, Wei & Huang, Xuan & Ding, Yinghui, 2014. "The role of fluctuating modes of autocorrelation in crude oil prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 382-390.
  • Handle: RePEc:eee:phsmap:v:393:y:2014:i:c:p:382-390
    DOI: 10.1016/j.physa.2013.08.055
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    References listed on IDEAS

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    1. M. E. J. Newman & D. J. Watts, 1999. "Renormalization Group Analysis of the Small-World Network Model," Working Papers 99-04-029, Santa Fe Institute.
    2. Asafu-Adjaye, John, 2000. "The relationship between energy consumption, energy prices and economic growth: time series evidence from Asian developing countries," Energy Economics, Elsevier, vol. 22(6), pages 615-625, December.
    3. Jiang, Zhibin & Yang, Huijie & Wang, Jianbo, 2009. "Complexities of human promoter sequences," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(7), pages 1299-1302.
    4. Meng-Cen Qian & Zhi-Qiang Jiang & Wei-Xing Zhou, 2009. "Universal and nonuniversal allometric scaling behaviors in the visibility graphs of world stock market indices," Papers 0910.2524, arXiv.org.
    5. Liu, Chuang & Zhou, Wei-Xing & Yuan, Wei-Kang, 2010. "Statistical properties of visibility graph of energy dissipation rates in three-dimensional fully developed turbulence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(13), pages 2675-2681.
    6. Yang, Yue & Wang, Jianbo & Yang, Huijie & Mang, Jingshi, 2009. "Visibility graph approach to exchange rate series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(20), pages 4431-4437.
    7. Alvarez-Ramirez, Jose & Cisneros, Myriam & Ibarra-Valdez, Carlos & Soriano, Angel, 2002. "Multifractal Hurst analysis of crude oil prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 313(3), pages 651-670.
    8. Yang, Yue & Yang, Huijie, 2008. "Complex network-based time series analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(5), pages 1381-1386.
    9. Wang, Na & Li, Dong & Wang, Qiwen, 2012. "Visibility graph analysis on quarterly macroeconomic series of China based on complex network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(24), pages 6543-6555.
    10. Adrangi, Bahram & Chatrath, Arjun & Dhanda, Kanwalroop Kathy & Raffiee, Kambiz, 2001. "Chaos in oil prices? Evidence from futures markets," Energy Economics, Elsevier, vol. 23(4), pages 405-425, July.
    11. Karimi, Somaye & Darooneh, Amir H., 2013. "Measuring persistence in a stationary time series using the complex network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(1), pages 287-293.
    12. Panas, Epaminondas & Ninni, Vassilia, 2000. "Are oil markets chaotic? A non-linear dynamic analysis," Energy Economics, Elsevier, vol. 22(5), pages 549-568, October.
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