IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2402.05364.html
   My bibliography  Save this paper

Coarse graining correlation matrices according to macrostructures: Financial markets as a paradigm

Author

Listed:
  • M. Mija'il Mart'inez-Ramos
  • Parisa Majari
  • Andres R. Cruz-Hern'andez
  • Hirdesh K. Pharasi
  • Manan Vyas

Abstract

We analyze correlation structures in financial markets by coarse graining the Pearson correlation matrices according to market sectors to obtain Guhr matrices using Guhr's correlation method according to Ref. [P. Rinn {\it et. al.}, Europhysics Letters 110, 68003 (2015)]. We compare the results for the evolution of market states and the corresponding transition matrices with those obtained using Pearson correlation matrices. The behavior of market states is found to be similar for both the coarse grained and Pearson matrices. However, the number of relevant variables is reduced by orders of magnitude.

Suggested Citation

  • M. Mija'il Mart'inez-Ramos & Parisa Majari & Andres R. Cruz-Hern'andez & Hirdesh K. Pharasi & Manan Vyas, 2024. "Coarse graining correlation matrices according to macrostructures: Financial markets as a paradigm," Papers 2402.05364, arXiv.org.
    • Handle: RePEc:arx:papers:2402.05364
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2402.05364
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. G. Bonanno & F. Lillo & R. N. Mantegna, 2001. "High-frequency cross-correlation in a set of stocks," Quantitative Finance, Taylor & Francis Journals, vol. 1(1), pages 96-104.
    2. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    3. Anton J. Heckens & Sebastian M. Krause & Thomas Guhr, 2020. "Uncovering the Dynamics of Correlation Structures Relative to the Collective Market Motion," Papers 2004.12336, arXiv.org, revised Sep 2020.
    4. Philip Rinn & Yuriy Stepanov & Joachim Peinke & Thomas Guhr & Rudi Schafer, 2015. "Dynamics of quasi-stationary systems: Finance as an example," Papers 1502.07522, arXiv.org.
    5. Giovanni Bonanno & Nicolas Vandewalle & Rosario N. Mantegna, 2000. "Taxonomy of Stock Market Indices," Papers cond-mat/0001268, arXiv.org, revised Aug 2000.
    6. Li Zhou & Lu Qiu & Changgui Gu & Huijie Yang, 2018. "Immediate Causality Network of Stock Markets," Papers 1802.02699, arXiv.org.
    7. Nick James & Max Menzies & Kevin Chin, 2022. "Economic state classification and portfolio optimisation with application to stagflationary environments," Papers 2203.15911, arXiv.org, revised Sep 2022.
    8. Michael C. Munnix & Takashi Shimada & Rudi Schafer & Francois Leyvraz Thomas H. Seligman & Thomas Guhr & H. E. Stanley, 2012. "Identifying States of a Financial Market," Papers 1202.1623, arXiv.org.
    9. James, Nick & Menzies, Max & Chin, Kevin, 2022. "Economic state classification and portfolio optimisation with application to stagflationary environments," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    10. Campbell, John Y. & Lo, Andrew W. & MacKinlay, A. Craig & Whitelaw, Robert F., 1998. "The Econometrics Of Financial Markets," Macroeconomic Dynamics, Cambridge University Press, vol. 2(4), pages 559-562, December.
    11. Hirdesh K. Pharasi & Kiran Sharma & Rakesh Chatterjee & Anirban Chakraborti & Francois Leyvraz & Thomas H. Seligman, 2018. "Identifying long-term precursors of financial market crashes using correlation patterns," Papers 1809.00885, arXiv.org, revised Sep 2018.
    12. Anton J. Heckens & Thomas Guhr, 2021. "A New Attempt to Identify Long-term Precursors for Endogenous Financial Crises in the Market Correlation Structures," Papers 2107.09048, arXiv.org, revised Aug 2022.
    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. Heckens, Anton J. & Guhr, Thomas, 2022. "New collectivity measures for financial covariances and correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    2. Martin He{ss}ler & Tobias Wand & Oliver Kamps, 2023. "Efficient Multi-Change Point Analysis to decode Economic Crisis Information from the S&P500 Mean Market Correlation," Papers 2308.00087, arXiv.org.
    3. Nick James & Max Menzies, 2023. "Collective dynamics, diversification and optimal portfolio construction for cryptocurrencies," Papers 2304.08902, arXiv.org, revised Jun 2023.
    4. James, Nick & Menzies, Max, 2023. "An exploration of the mathematical structure and behavioural biases of 21st century financial crises," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    5. 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).
    6. Sandoval, Leonidas, 2014. "To lag or not to lag? How to compare indices of stock markets that operate on different times," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 403(C), pages 227-243.
    7. James, Nick & Menzies, Max, 2023. "Collective infectivity of the pandemic over time and association with vaccine coverage and economic development," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    8. Zhang, Xin & Podobnik, Boris & Kenett, Dror Y. & Eugene Stanley, H., 2014. "Systemic risk and causality dynamics of the world international shipping market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 415(C), pages 43-53.
    9. Jiang, Zhi-Qiang & Zhou, Wei-Xing, 2010. "Complex stock trading network among investors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4929-4941.
    10. Tumminello, Michele & Lillo, Fabrizio & Mantegna, Rosario N., 2010. "Correlation, hierarchies, and networks in financial markets," Journal of Economic Behavior & Organization, Elsevier, vol. 75(1), pages 40-58, July.
    11. Sensoy, Ahmet & Tabak, Benjamin M., 2014. "Dynamic spanning trees in stock market networks: The case of Asia-Pacific," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 387-402.
    12. Tao You & Paweł Fiedor & Artur Hołda, 2015. "Network Analysis of the Shanghai Stock Exchange Based on Partial Mutual Information," JRFM, MDPI, vol. 8(2), pages 1-19, June.
    13. Mario L'opez P'erez & Ricardo Mansilla, 2021. "Ordinal Synchronization and Typical States in High-Frequency Digital Markets," Papers 2110.07047, arXiv.org, revised Mar 2022.
    14. Song, Dong-Ming & Jiang, Zhi-Qiang & Zhou, Wei-Xing, 2009. "Statistical properties of world investment networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(12), pages 2450-2460.
    15. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    16. Naylor, Michael J. & Rose, Lawrence C. & Moyle, Brendan J., 2007. "Topology of foreign exchange markets using hierarchical structure methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(1), pages 199-208.
    17. Tu, Chengyi, 2014. "Cointegration-based financial networks study in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 402(C), pages 245-254.
    18. Antonio Briola & Tomaso Aste, 2022. "Dependency structures in cryptocurrency market from high to low frequency," Papers 2206.03386, arXiv.org, revised Dec 2022.
    19. Sandoval, Leonidas & Franca, Italo De Paula, 2012. "Correlation of financial markets in times of crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 187-208.
    20. Hirdesh K. Pharasi & Suchetana Sadhukhan & Parisa Majari & Anirban Chakraborti & Thomas H. Seligman, 2021. "Dynamics of the market states in the space of correlation matrices with applications to financial markets," Papers 2107.05663, arXiv.org.

    More about this item

    Statistics

    Access and download statistics

    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:2402.05364. 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.