IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v537y2020ics0378437119314785.html
   My bibliography  Save this article

Complexity analysis of time series based on generalized fractional order cumulative residual distribution entropy

Author

Listed:
  • Wang, Yu
  • Shang, Pengjian

Abstract

Based on the theory of cumulative residual entropy and distribution entropy, this paper proposes a new model—cumulative residual distribution entropy (CRDE), which is more suitable for the complexity and risk analysis of time series The new model makes full use of the known information, including not only the information of probability, but also the information about the value of random variables. Additionally, the CRDE considers the potential inherent information of vector-to-vector distance in the state space. By combining theoretical analysis with empirical research, this paper verifies that the new model has more advantages in measuring extreme events and small probability events, with higher consistency, stability and practicability. In this paper, the cumulative residual distribution entropy model is extended to the fractional order. The generalized fractional cumulative residual distribution entropy (GCRDE) can better capture the tiny evolution of time series, which is more advantageous for studying the dynamic characteristics of complex systems. The new model makes up for some shortcomings of traditional models and it can play a guiding role in the study of complex systems in the real world.

Suggested Citation

  • Wang, Yu & Shang, Pengjian, 2020. "Complexity analysis of time series based on generalized fractional order cumulative residual distribution entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    • Handle: RePEc:eee:phsmap:v:537:y:2020:i:c:s0378437119314785
    DOI: 10.1016/j.physa.2019.122582
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437119314785
    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.2019.122582?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. Jianshe Ou, 2005. "Theory of portfolio and risk based on incremental entropy," Journal of Risk Finance, Emerald Group Publishing, vol. 6(1), pages 31-39, January.
    2. A. Dionisio & R. Menezes & D. A. Mendes, 2006. "An econophysics approach to analyse uncertainty in financial markets: an application to the Portuguese stock market," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 50(1), pages 161-164, March.
    3. Maasoumi, Esfandiar & Racine, Jeff, 2002. "Entropy and predictability of stock market returns," Journal of Econometrics, Elsevier, vol. 107(1-2), pages 291-312, March.
    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. Saha, Shital & Kayal, Suchandan, 2023. "Extended fractional cumulative past and paired ϕ-entropy measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 614(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. Busu, Cristian & Busu, Mihail, 2019. "Modeling the predictive power of the singular value decomposition-based entropy. Empirical evidence from the Dow Jones Global Titans 50 Index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    2. Daniel Chiew & Judy Qiu & Sirimon Treepongkaruna & Jiping Yang & Chenxiao Shi, 2019. "The predictive ability of the expected utility-entropy based fund rating approach: A comparison investigation with Morningstar ratings in US," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-22, April.
    3. Mihály Ormos & Dávid Zibriczky, 2014. "Entropy-Based Financial Asset Pricing," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-21, December.
    4. Gruber, Lutz F. & West, Mike, 2017. "Bayesian online variable selection and scalable multivariate volatility forecasting in simultaneous graphical dynamic linear models," Econometrics and Statistics, Elsevier, vol. 3(C), pages 3-22.
    5. Andrey Shternshis & Piero Mazzarisi & Stefano Marmi, 2022. "Efficiency of the Moscow Stock Exchange before 2022," Papers 2207.10476, arXiv.org, revised Jul 2022.
    6. Salois, Matthew & Moss, Charles, 2010. "An Information Approach to the Dynamics in Farm Income: Implications for Farmland Markets," MPRA Paper 26850, University Library of Munich, Germany.
    7. Piotr Fiszeder & Witold Orzeszko, 2012. "Nonparametric Verification of GARCH-Class Models for Selected Polish Exchange Rates and Stock Indices," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 62(5), pages 430-449, November.
    8. Dionisio, Andreia & Menezes, Rui & Mendes, Diana & Vidigal Da Silva, Jacinto, 2007. "Nonlinear Dynamics Within Macroeconomic Factors And Stock Market In Portugal, 1993-2003," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 7(2), pages 57-70.
    9. Dora Almeida & Andreia Dionísio & Isabel Vieira & Paulo Ferreira, 2022. "Uncertainty and Risk in the Cryptocurrency Market," JRFM, MDPI, vol. 15(11), pages 1-17, November.
    10. Stefania D'Amico, 2004. "Density Estimation and Combination under Model Ambiguity," Computing in Economics and Finance 2004 273, Society for Computational Economics.
    11. Tseng, Chih-Hsiung & Cheng, Sheng-Tzong & Wang, Yi-Hsien & Peng, Jin-Tang, 2008. "Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3192-3200.
    12. Luca Bagnato & Valerio Potì & Maria Zoia, 2015. "The role of orthogonal polynomials in adjusting hyperpolic secant and logistic distributions to analyse financial asset returns," Statistical Papers, Springer, vol. 56(4), pages 1205-1234, November.
    13. Kang, Yanfei & Hyndman, Rob J. & Smith-Miles, Kate, 2017. "Visualising forecasting algorithm performance using time series instance spaces," International Journal of Forecasting, Elsevier, vol. 33(2), pages 345-358.
    14. Mastroeni, Loretta & Mazzoccoli, Alessandro & Vellucci, Pierluigi, 2024. "Wavelet entropy and complexity–entropy curves approach for energy commodity price predictability amid the transition to alternative energy sources," Chaos, Solitons & Fractals, Elsevier, vol. 184(C).
    15. Kwang-il Choe & Joshua Krausz & Kiseok Nam, 2011. "Technical trading rules for nonlinear dynamics of stock returns: evidence from the G-7 stock markets," Review of Quantitative Finance and Accounting, Springer, vol. 36(3), pages 323-353, April.
    16. Vishwas Kukreti & Hirdesh K. Pharasi & Priya Gupta & Sunil Kumar, 2020. "A perspective on correlation-based financial networks and entropy measures," Papers 2004.09448, arXiv.org.
    17. Ferreira, Paulo, 2015. "Entropy, competitiveness and UEFA football ranking," MPRA Paper 63132, University Library of Munich, Germany.
    18. Casarin, Roberto & Costola, Michele, 2019. "Structural changes in large economic datasets: A nonparametric homogeneity test," Economics Letters, Elsevier, vol. 176(C), pages 55-59.
    19. Esfandiar Maasoumi & Melinda Pitts & Ke Wu, 2014. "The Gap between the Conditional Wage Distributions of Incumbents and the Newly Hired Employees: Decomposition and Uniform Ordering," Advances in Econometrics, in: Essays in Honor of Peter C. B. Phillips, volume 33, pages 587-612, Emerald Group Publishing Limited.
    20. Syed Jawad Hussain Shahzad & Dene Hurley & Román Ferrer, 2021. "U.S. stock prices and macroeconomic fundamentals: Fresh evidence using the quantile ARDL approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 3569-3587, July.

    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:537:y:2020:i:c:s0378437119314785. 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.