IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v494y2018icp389-402.html
   My bibliography  Save this article

Analysis of crude oil markets with improved multiscale weighted permutation entropy

Author

Listed:
  • Niu, Hongli
  • Wang, Jun
  • Liu, Cheng

Abstract

Entropy measures are recently extensively used to study the complexity property in nonlinear systems. Weighted permutation entropy (WPE) can overcome the ignorance of the amplitude information of time series compared with PE and shows a distinctive ability to extract complexity information from data having abrupt changes in magnitude. Improved (or sometimes called composite) multi-scale (MS) method possesses the advantage of reducing errors and improving the accuracy when applied to evaluate multiscale entropy values of not enough long time series. In this paper, we combine the merits of WPE and improved MS to propose the improved multiscale weighted permutation entropy (IMWPE) method for complexity investigation of a time series. Then it is validated effective through artificial data: white noise and 1∕f noise, and real market data of Brent and Daqing crude oil. Meanwhile, the complexity properties of crude oil markets are explored respectively of return series, volatility series with multiple exponents and EEMD-produced intrinsic mode functions (IMFs) which represent different frequency components of return series. Moreover, the instantaneous amplitude and frequency of Brent and Daqing crude oil are analyzed by the Hilbert transform utilized to each IMF.

Suggested Citation

  • Niu, Hongli & Wang, Jun & Liu, Cheng, 2018. "Analysis of crude oil markets with improved multiscale weighted permutation entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 389-402.
    • Handle: RePEc:eee:phsmap:v:494:y:2018:i:c:p:389-402
    DOI: 10.1016/j.physa.2017.12.049
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437117312918
    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.2017.12.049?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. Aghamohammadi, Cina & Ebrahimian, Mehran & Tahmooresi, Hamed, 2014. "Permutation approach, high frequency trading and variety of micro patterns in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 25-30.
    2. Matilla-García, Mariano & Marín, Manuel Ruiz & Dore, Mohammed I., 2014. "A permutation entropy based test for causality: The volume–stock price relation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 398(C), pages 280-288.
    3. Majapa, Mohamed & Gossel, Sean Joss, 2016. "Topology of the South African stock market network across the 2008 financial crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 445(C), pages 35-47.
    4. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
    5. Paulo Ferreira & Andreia Dionísio & Cesaltina Pires, 2010. "Adopt the euro? The GME approach," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 5(2), pages 231-247, December.
    6. García, A., 2016. "Global financial indices and twitter sentiment: A random matrix theory approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 509-522.
    7. Saco, Patricia M. & Carpi, Laura C. & Figliola, Alejandra & Serrano, Eduardo & Rosso, Osvaldo A., 2010. "Entropy analysis of the dynamics of El Niño/Southern Oscillation during the Holocene," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 5022-5027.
    8. Zunino, Luciano & Zanin, Massimiliano & Tabak, Benjamin M. & Pérez, Darío G. & Rosso, Osvaldo A., 2009. "Forbidden patterns, permutation entropy and stock market inefficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(14), pages 2854-2864.
    9. Zunino, Luciano & Zanin, Massimiliano & Tabak, Benjamin M. & Pérez, Darío G. & Rosso, Osvaldo A., 2010. "Complexity-entropy causality plane: A useful approach to quantify the stock market inefficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(9), pages 1891-1901.
    10. Niu, Hongli & Wang, Jun & Lu, Yunfan, 2016. "Fluctuation behaviors of financial return volatility duration," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 448(C), pages 30-40.
    11. Chen, Xin & Jin, Ning-De & Zhao, An & Gao, Zhong-Ke & Zhai, Lu-Sheng & Sun, Bin, 2015. "The experimental signals analysis for bubbly oil-in-water flow using multi-scale weighted-permutation entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 230-244.
    12. Xavier Gabaix & Parameswaran Gopikrishnan & Vasiliki Plerou & H. Eugene Stanley, 2003. "A theory of power-law distributions in financial market fluctuations," Nature, Nature, vol. 423(6937), pages 267-270, May.
    13. Cina Aghamohammadi & Mehran Ebrahimian & Hamed Tahmooresi, 2014. "Permutation approach, high frequency trading and variety of micro patterns in financial time series," Papers 1407.5254, arXiv.org.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Yali & Wang, Jun, 2019. "Linkage influence of energy market on financial market by multiscale complexity synchronization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 254-266.
    2. Sun, Jie & Zhao, Xiaojun & Xu, Chao, 2021. "Crude oil market autocorrelation: Evidence from multiscale quantile regression analysis," Energy Economics, Elsevier, vol. 98(C).
    3. Liu, Hongzhi & Zhang, Xie & Hu, Huaqing & Zhang, Xingchen, 2022. "Exploring the impact of flow values on multiscale complexity quantification of airport flight flow fluctuations," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    4. Han, Yun-Feng & Jin, Ning-De & Zhai, Lu-Sheng & Ren, Ying-Yu & He, Yuan-Sheng, 2019. "An investigation of oil–water two-phase flow instability using multivariate multi-scale weighted permutation entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 518(C), pages 131-144.
    5. Chengzhao, Zhang & Heping, Pan & Yu, Ma & Xun, Huang, 2019. "Analysis of Asia Pacific stock markets with a novel multiscale model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).

    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. Wang, Guochao & Zheng, Shenzhou & Wang, Jun, 2019. "Complex and composite entropy fluctuation behaviors of statistical physics interacting financial model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 97-113.
    2. Wang, Guochao & Zheng, Shenzhou & Wang, Jun, 2020. "Fluctuation and volatility dynamics of stochastic interacting energy futures price model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    3. Gu, Rongbao & Xiong, Wei & Li, Xinjie, 2015. "Does the singular value decomposition entropy have predictive power for stock market? — Evidence from the Shenzhen stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 439(C), pages 103-113.
    4. Rosso, Osvaldo A. & Carpi, Laura C. & Saco, Patricia M. & Gómez Ravetti, Martín & Plastino, Angelo & Larrondo, Hilda A., 2012. "Causality and the entropy–complexity plane: Robustness and missing ordinal patterns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 42-55.
    5. De Micco, Luciana & Fernández, Juana Graciela & Larrondo, Hilda A. & Plastino, Angelo & Rosso, Osvaldo A., 2012. "Sampling period, statistical complexity, and chaotic attractors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(8), pages 2564-2575.
    6. Zhang, Yongping & Shang, Pengjian & Xiong, Hui, 2019. "Multivariate generalized information entropy of financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1212-1223.
    7. López Pérez, Mario & Mansilla Corona, Ricardo, 2022. "Ordinal synchronization and typical states in high-frequency digital markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    8. Shahzad, Syed Jawad Hussain & Hernandez, Jose Areola & Hanif, Waqas & Kayani, Ghulam Mujtaba, 2018. "Intraday return inefficiency and long memory in the volatilities of forex markets and the role of trading volume," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 433-450.
    9. Fernandes, Leonardo H.S. & de Araújo, Fernando H.A. & Silva, Igor E.M. & Neto, Jusie S.P., 2021. "Macroeconophysics indicator of economic efficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    10. Kostanjcar, Zvonko & Jeren, Branko & Juretic, Zeljan, 2012. "Impact of uncertainty in expected return estimation on stock price volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5563-5571.
    11. Higor Y. D. Sigaki & Matjaz Perc & Haroldo V. Ribeiro, 2019. "Clustering patterns in efficiency and the coming-of-age of the cryptocurrency market," Papers 1901.04967, arXiv.org.
    12. Vygintas Gontis, 2021. "Order flow in the financial markets from the perspective of the Fractional L\'evy stable motion," Papers 2105.02057, arXiv.org, revised Nov 2021.
    13. Zhao, Xiaojun & Ji, Mengfan & Zhang, Na & Shang, Pengjian, 2020. "Permutation transition entropy: Measuring the dynamical complexity of financial time series," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    14. Chen, Shijian & Shang, Pengjian & Wu, Yue, 2019. "Multivariate multiscale fractional order weighted permutation entropy of nonlinear time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 217-231.
    15. Christopher M Wray & Steven R Bishop, 2016. "A Financial Market Model Incorporating Herd Behaviour," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-28, March.
    16. Bariviera, Aurelio F. & Guercio, M. Belén & Martinez, Lisana B. & Rosso, Osvaldo A., 2016. "Libor at crossroads: Stochastic switching detection using information theory quantifiers," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 172-182.
    17. Zunino, Luciano & Fernández Bariviera, Aurelio & Guercio, M. Belén & Martinez, Lisana B. & Rosso, Osvaldo A., 2012. "On the efficiency of sovereign bond markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(18), pages 4342-4349.
    18. Luiz G. A. Alves & Higor Y. D. Sigaki & Matjaz Perc & Haroldo V. Ribeiro, 2020. "Collective dynamics of stock market efficiency," Papers 2011.14809, arXiv.org.
    19. Niu, Hongli & Wang, Weiqing & Zhang, Junhuan, 2019. "Recurrence duration statistics and time-dependent intrinsic correlation analysis of trading volumes: A study of Chinese stock indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 838-854.
    20. Obanya, Praise Otito & Seitshiro, Modisane & Olivier, Carel Petrus & Verster, Tanja, 2024. "A permutation entropy analysis of Bitcoin volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(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:494:y:2018:i:c:p:389-402. 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.