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Uncovering the Dynamics of Correlation Structures Relative to the Collective Market Motion

Author

Listed:
  • Anton J. Heckens
  • Sebastian M. Krause
  • Thomas Guhr

Abstract

The measured correlations of financial time series in subsequent epochs change considerably as a function of time. When studying the whole correlation matrices, quasi-stationary patterns, referred to as market states, are seen by applying clustering methods. They emerge, disappear or reemerge, but they are dominated by the collective motion of all stocks. In the jargon, one speaks of the market motion, it is always associated with the largest eigenvalue of the correlation matrices. Thus the question arises, if one can extract more refined information on the system by subtracting the dominating market motion in a proper way. To this end we introduce a new approach by clustering reduced-rank correlation matrices which are obtained by subtracting the dyadic matrix belonging to the largest eigenvalue from the standard correlation matrices. We analyze daily data of 262 companies of the S&P 500 index over a period of almost 15 years from 2002 to 2016. The resulting dynamics is remarkably different, and the corresponding market states are quasi-stationary over a long period of time. Our approach adds to the attempts to separate endogenous from exogenous effects.

Suggested Citation

  • 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.
    • Handle: RePEc:arx:papers:2004.12336
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    Cited by:

    1. 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).
    2. 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.
    3. 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).
    4. James, Nick & Menzies, Max & Gottwald, Georg A., 2022. "On financial market correlation structures and diversification benefits across and within equity sectors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    5. Nick James & Max Menzies, 2023. "An exploration of the mathematical structure and behavioural biases of 21st century financial crises," Papers 2307.15402, arXiv.org, revised Sep 2023.
    6. 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).
    7. Nick James & Max Menzies, 2021. "Collective correlations, dynamics, and behavioural inconsistencies of the cryptocurrency market over time," Papers 2107.13926, arXiv.org, revised Dec 2021.
    8. 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.
    9. Nick James, 2021. "Dynamics, behaviours, and anomaly persistence in cryptocurrencies and equities surrounding COVID-19," Papers 2101.00576, arXiv.org, revised Feb 2021.
    10. Nick James & Max Menzies & Georg A. Gottwald, 2022. "On financial market correlation structures and diversification benefits across and within equity sectors," Papers 2202.10623, arXiv.org, revised Jun 2022.
    11. 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.
    12. 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.
    13. 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.
    14. 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).
    15. James, Nick & Chin, Kevin, 2022. "On the systemic nature of global inflation, its association with equity markets and financial portfolio implications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    16. 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.
    17. Nick James & Max Menzies, 2023. "Collective dynamics, diversification and optimal portfolio construction for cryptocurrencies," Papers 2304.08902, arXiv.org, revised Jun 2023.
    18. 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).
    19. James, Nick, 2021. "Dynamics, behaviours, and anomaly persistence in cryptocurrencies and equities surrounding COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
    20. 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).
    21. Nick James, 2021. "Evolutionary correlation, regime switching, spectral dynamics and optimal trading strategies for cryptocurrencies and equities," Papers 2112.15321, arXiv.org, revised Mar 2022.

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