IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2107.05663.html
   My bibliography  Save this paper

Dynamics of the market states in the space of correlation matrices with applications to financial markets

Author

Listed:
  • Hirdesh K. Pharasi
  • Suchetana Sadhukhan
  • Parisa Majari
  • Anirban Chakraborti
  • Thomas H. Seligman

Abstract

The concept of states of financial markets based on correlations has gained increasing attention during the last 10 years. We propose to retrace some important steps up to 2018, and then give a more detailed view of recent developments that attempt to make the use of this more practical. Finally, we try to give a glimpse to the future proposing the analysis of trajectories in correlation matrix space directly or in terms of symbolic dynamics as well as attempts to analyze the clusters that make up the states in a random matrix context.

Suggested Citation

  • 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.
    • Handle: RePEc:arx:papers:2107.05663
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2107.05663
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Frederik Meudt & Martin Theissen & Rudi Schafer & Thomas Guhr, 2015. "Constructing Analytically Tractable Ensembles of Non-Stationary Covariances with an Application to Financial Data," Papers 1503.01584, arXiv.org, revised Jul 2015.
    2. 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.
    3. 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.
    4. Yuriy Stepanov & Philip Rinn & Thomas Guhr & Joachim Peinke & Rudi Schafer, 2015. "Stability and Hierarchy of Quasi-Stationary States: Financial Markets as an Example," Papers 1503.00556, arXiv.org.
    5. Hirdesh K. Pharasi & Eduard Seligman & Thomas H. Seligman, 2020. "Market states: A new understanding," Papers 2003.07058, arXiv.org, revised Nov 2020.
    6. Thomas Guhr & Andreas Schell, 2020. "Exact Multivariate Amplitude Distributions for Non-Stationary Gaussian or Algebraic Fluctuations of Covariances or Correlations," Papers 2011.07570, arXiv.org.
    7. 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.
    8. Warren Torgerson, 1952. "Multidimensional scaling: I. Theory and method," Psychometrika, Springer;The Psychometric Society, vol. 17(4), pages 401-419, December.
    9. Rudi Schafer & Nils Fredrik Nilsson & Thomas Guhr, 2010. "Power mapping with dynamical adjustment for improved portfolio optimization," Quantitative Finance, Taylor & Francis Journals, vol. 10(1), pages 107-119.
    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. 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).

    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. Pharasi, Hirdesh K. & Seligman, Eduard & Sadhukhan, Suchetana & Majari, Parisa & Seligman, Thomas H., 2024. "Dynamics of market states and risk assessment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    2. 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).
    3. 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.
    4. 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, revised Jun 2024.
    5. Gartzke, Sebastian & Wang, Shanshan & Guhr, Thomas & Schreckenberg, Michael, 2022. "Spatial correlation analysis of traffic flow on parallel motorways in Germany," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    6. Tobias Wand & Oliver Kamps & Hiroshi Iyetomi, 2024. "Causal Hierarchy in the Financial Market Network -- Uncovered by the Helmholtz-Hodge-Kodaira Decomposition," Papers 2408.12839, arXiv.org.
    7. 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.
    8. Hirdesh K. Pharasi & Eduard Seligman & Suchetana Sadhukhan & Parisa Majari & Thomas H. Seligman, 2020. "Dynamics of market states and risk assessment," Papers 2011.05984, arXiv.org, revised Sep 2023.
    9. Hirdesh K. Pharasi & Kiran Sharma & Anirban Chakraborti & Thomas H. Seligman, 2018. "Complex market dynamics in the light of random matrix theory," Papers 1809.07100, arXiv.org, revised Sep 2018.
    10. Alexander Strehl & Joydeep Ghosh, 2003. "Relationship-Based Clustering and Visualization for High-Dimensional Data Mining," INFORMS Journal on Computing, INFORMS, vol. 15(2), pages 208-230, May.
    11. 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).
    12. Tobias Wand & Martin He{ss}ler & Oliver Kamps, 2022. "Identifying Dominant Industrial Sectors in Market States of the S&P 500 Financial Data," Papers 2208.14106, arXiv.org, revised Mar 2023.
    13. Venera Tomaselli, 1996. "Multivariate statistical techniques and sociological research," Quality & Quantity: International Journal of Methodology, Springer, vol. 30(3), pages 253-276, August.
    14. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "A new approach for the quantification of qualitative measures of economic expectations," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(6), pages 2685-2706, November.
    15. Bijmolt, T.H.A. & Wedel, M., 1996. "A Monte Carlo Evaluation of Maximum Likelihood Multidimensional Scaling Methods," Other publications TiSEM f72cc9d8-f370-43aa-a224-4, Tilburg University, School of Economics and Management.
    16. Andreas Muhlbacher & Thomas Guhr, 2018. "Credit Risk Meets Random Matrices: Coping with Non-Stationary Asset Correlations," Papers 1803.00261, arXiv.org.
    17. Nick James & Kevin Chin, 2021. "On the systemic nature of global inflation, its association with equity markets and financial portfolio implications," Papers 2111.11022, arXiv.org, revised Jan 2022.
    18. Jinkai Yu & Wenjing Bi, 2019. "Evolution of Marine Environmental Governance Policy in China," Sustainability, MDPI, vol. 11(18), pages 1-14, September.
    19. Walesiak Marek & Dudek Andrzej, 2017. "Selecting the Optimal Multidimensional Scaling Procedure for Metric Data With R Environment," Statistics in Transition New Series, Polish Statistical Association, vol. 18(3), pages 521-540, September.
    20. Mirta Galesic & A. Walkyria Goode & Thomas S. Wallsten & Kent L. Norman, 2018. "Using Tversky’s contrast model to investigate how features of similarity affect judgments of likelihood," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 13(2), pages 163-169, March.

    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:2107.05663. 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.