IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v393y2014icp382-390.html
   My bibliography  Save this article

The role of fluctuating modes of autocorrelation in crude oil prices

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437113008054
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2013.08.055?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dai, Peng-Fei & Xiong, Xiong & Zhou, Wei-Xing, 2019. "Visibility graph analysis of economy policy uncertainty indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    2. Liu, Hao-Ran & Li, Ming-Xia & Zhou, Wei-Xing, 2024. "Visibility graph analysis of the grains and oilseeds indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 650(C).
    3. Tang, Jinjun & Wang, Yinhai & Wang, Hua & Zhang, Shen & Liu, Fang, 2014. "Dynamic analysis of traffic time series at different temporal scales: A complex networks approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 303-315.
    4. Wang, Minggang & Tian, Lixin, 2016. "From time series to complex networks: The phase space coarse graining," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 456-468.
    5. Alvarez-Ramirez, Jose & Alvarez, Jesus & Rodriguez, Eduardo, 2008. "Short-term predictability of crude oil markets: A detrended fluctuation analysis approach," Energy Economics, Elsevier, vol. 30(5), pages 2645-2656, September.
    6. Yao, Can-Zhong & Lin, Ji-Nan & Zheng, Xu-Zhou & Liu, Xiao-Feng, 2015. "The study of RMB exchange rate complex networks based on fluctuation mode," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 359-376.
    7. He, Ling-Yun & Fan, Ying & Wei, Yi-Ming, 2009. "Impact of speculator's expectations of returns and time scales of investment on crude oil price behaviors," Energy Economics, Elsevier, vol. 31(1), pages 77-84, January.
    8. Gu, Rongbao & Zhang, Bing, 2016. "Is efficiency of crude oil market affected by multifractality? Evidence from the WTI crude oil market," Energy Economics, Elsevier, vol. 53(C), pages 151-158.
    9. Ömer Akgüller & Mehmet Ali Balcı & Larissa M. Batrancea & Lucian Gaban, 2023. "Path-Based Visibility Graph Kernel and Application for the Borsa Istanbul Stock Network," Mathematics, MDPI, vol. 11(6), pages 1-25, March.
    10. Fan, Ying & Liang, Qiang & Wei, Yi-Ming, 2008. "A generalized pattern matching approach for multi-step prediction of crude oil price," Energy Economics, Elsevier, vol. 30(3), pages 889-904, May.
    11. Xu, Paiheng & Zhang, Rong & Deng, Yong, 2018. "A novel visibility graph transformation of time series into weighted networks," Chaos, Solitons & Fractals, Elsevier, vol. 117(C), pages 201-208.
    12. Tabak, Benjamin M. & Cajueiro, Daniel O., 2007. "Are the crude oil markets becoming weakly efficient over time? A test for time-varying long-range dependence in prices and volatility," Energy Economics, Elsevier, vol. 29(1), pages 28-36, January.
    13. Xie, Wen-Jie & Zhou, Wei-Xing, 2011. "Horizontal visibility graphs transformed from fractional Brownian motions: Topological properties versus the Hurst index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(20), pages 3592-3601.
    14. Mondal, Mitali & Mondal, Arindam & Mondal, Joyati & Patra, Kanchan Kumar & Deb, Argha & Ghosh, Dipak, 2018. "Evidence of centrality dependent fractal behavior in high energy heavy ion interactions: Hint of two different sources," Chaos, Solitons & Fractals, Elsevier, vol. 113(C), pages 230-237.
    15. Hongtao Chen & Lianghua Chen, 2015. "Multifractal spectrum analysis of Brent crude oil futures prices volatility in intercontinental exchange," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 38(1/2/3), pages 93-108.
    16. Sainaghi, Ruggero & Baggio, Rodolfo, 2017. "Complexity traits and dynamics of tourism destinations," Tourism Management, Elsevier, vol. 63(C), pages 368-382.
    17. Chen, Shiyu & Hu, Yong & Mahadevan, Sankaran & Deng, Yong, 2014. "A visibility graph averaging aggregation operator," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 403(C), pages 1-12.
    18. He, Ling-Yun & Qian, Wen-Bin, 2012. "A Monte Carlo simulation to the performance of the R/S and V/S methods—Statistical revisit and real world application," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(14), pages 3770-3782.
    19. Gu, Rongbao & Chen, Hongtao & Wang, Yudong, 2010. "Multifractal analysis on international crude oil markets based on the multifractal detrended fluctuation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(14), pages 2805-2815.
    20. Liu, Keshi & Weng, Tongfeng & Gu, Changgui & Yang, Huijie, 2020. "Visibility graph analysis of Bitcoin price series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 538(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:393:y:2014:i:c:p:382-390. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.