IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1501.02216.html
   My bibliography  Save this paper

Analyses of Statistical Structures in Economic Indices

Author

Listed:
  • Frank W. K. Firk

Abstract

The complex, time-dependent statistical structures observed in the Dow Jones Industrial Average on a typical trading day are modeled with Lorentzian functions. The resonant-like structures are characterized by the values of the basic ratio: the average lifetime of the individual states associated with a given structural form divided by the average interval between adjacent states. Values of the ratio are determined for three structural forms characterized by the average intervals: 50 to 100 seconds (the fine structure), approximately10 minutes, and approximately1 hour (the intermediate structures I and II). During the trading day the values of the basic ratio associated with the fine structure of the index are found to lie in the narrow range from 0.49 to 0.52. This finding is characteristic of the highly statistical nature of many-body systems typified by daily trading. It is therefore proposed that the value of this ratio, determined in the first hour-or-so on a given day, be used to provide information concerning the likely performance of the fine, statistical component of the index for the remainder of the trading day. For the intermediate structures the basic ratios are approximately 0.6 and therefore they too can be analyzed as individual states. Keywords: Analytical economics; Lorentzian analyses of statistical structures in the Dow Jones Industrial Average; basic parameters of economic indices.

Suggested Citation

  • Frank W. K. Firk, 2014. "Analyses of Statistical Structures in Economic Indices," Papers 1501.02216, arXiv.org.
    • Handle: RePEc:arx:papers:1501.02216
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1501.02216
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Plerou, V & Gopikrishnan, P & Rosenow, B & Amaral, L.A.N & Stanley, H.E, 2000. "A random matrix theory approach to financial cross-correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 287(3), pages 374-382.
    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. Barunik, Jozef & Vacha, Lukas, 2010. "Monte Carlo-based tail exponent estimator," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4863-4874.
    2. Stosic, Darko & Stosic, Dusan & Ludermir, Teresa B. & Stosic, Tatijana, 2018. "Collective behavior of cryptocurrency price changes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 499-509.
    3. 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.
    4. Ormerod, Paul, 2008. "Random Matrix Theory and Macro-Economic Time-Series: An Illustration Using the Evolution of Business Cycle Synchronisation, 1886-2006," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 2, pages 1-10.
    5. Duc Thi Luu, 2022. "Portfolio Correlations in the Bank-Firm Credit Market of Japan," Computational Economics, Springer;Society for Computational Economics, vol. 60(2), pages 529-569, August.
    6. Istvan Varga-Haszonits & Fabio Caccioli & Imre Kondor, 2016. "Replica approach to mean-variance portfolio optimization," Papers 1606.08679, arXiv.org.
    7. Dai, Yun-Shi & Huynh, Ngoc Quang Anh & Zheng, Qing-Huan & Zhou, Wei-Xing, 2022. "Correlation structure analysis of the global agricultural futures market," Research in International Business and Finance, Elsevier, vol. 61(C).
    8. Tetsuya Takaishi, 2016. "Dynamical cross-correlation of multiple time series Ising model," Evolutionary and Institutional Economics Review, Springer, vol. 13(2), pages 455-468, December.
    9. Jovanovic, Franck & Mantegna, Rosario N. & Schinckus, Christophe, 2019. "When financial economics influences physics: The role of Econophysics," International Review of Financial Analysis, Elsevier, vol. 65(C).
    10. G'abor Papp & Fabio Caccioli & Imre Kondor, 2016. "Bias-variance trade-off in portfolio optimization under Expected Shortfall with $\ell_2$ regularization," Papers 1602.08297, arXiv.org, revised Jul 2018.
    11. Zhaoyuan Li & Maozai Tian, 2017. "A New Method For Dynamic Stock Clustering Based On Spectral Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 50(3), pages 373-392, October.
    12. Daly, J. & Crane, M. & Ruskin, H.J., 2008. "Random matrix theory filters in portfolio optimisation: A stability and risk assessment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(16), pages 4248-4260.
    13. Fabio Caccioli & Imre Kondor & G'abor Papp, 2015. "Portfolio Optimization under Expected Shortfall: Contour Maps of Estimation Error," Papers 1510.04943, arXiv.org.
    14. Dror Y Kenett & Matthias Raddant & Thomas Lux & Eshel Ben-Jacob, 2012. "Evolvement of Uniformity and Volatility in the Stressed Global Financial Village," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-8, February.
    15. Lux, Thomas, 2008. "Applications of statistical physics in finance and economics," Kiel Working Papers 1425, Kiel Institute for the World Economy (IfW Kiel).
    16. Dusan Stosic & Darko Stosic & Tatijana Stosic, 2019. "Nonextensive triplets in stock market indices," Papers 1901.07721, arXiv.org.
    17. Juan Pineiro-Chousa & Marcos Vizcaíno-González & Jérôme Caby, 2016. "Analysing voting behaviour in the United States banking sector through eigenvalue decomposition," Applied Economics Letters, Taylor & Francis Journals, vol. 23(12), pages 840-843, August.
    18. Sharifi, S. & Crane, M. & Shamaie, A. & Ruskin, H., 2004. "Random matrix theory for portfolio optimization: a stability approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 335(3), pages 629-643.
    19. Stosic, Dusan & Stosic, Darko & Stosic, Tatijana, 2019. "Nonextensive triplets in stock market indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 192-198.
    20. Eterovic, Nicolas A. & Eterovic, Dalibor S., 2013. "Separating the wheat from the chaff: Understanding portfolio returns in an emerging market," Emerging Markets Review, Elsevier, vol. 16(C), pages 145-169.

    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:arx:papers:1501.02216. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.