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A New Attempt to Identify Long-term Precursors for Endogenous Financial Crises in the Market Correlation Structures

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  • Anton J. Heckens
  • Thomas Guhr

Abstract

Prediction of events in financial markets is every investor's dream and, usually, wishful thinking. From a more general, economic and societal viewpoint, the identification of indicators for large events is highly desirable to assess systemic risks. Unfortunately, the very nature of financial markets, particularly the predominantly non-Markovian character as well as non-stationarity, make this challenge a formidable one, leaving little hope for fully fledged answers. Nevertheless, it is called for to collect pieces of evidence in a variety of observables to be assembled like the pieces of a puzzle that eventually might help to catch a glimpse of long-term indicators or precursors for large events - if at all in a statistical sense. Here, we present a new piece for this puzzle. We use the quasi-stationary market states which exist in the time evolution of the correlation structure in financial markets. Recently, we identified such market states relative to the collective motion of the market as a whole. We study their precursor properties in the US stock markets over 16 years, including two endogenous crises, the dot-com bubble burst and the pre-phase of the Lehman Brothers crash. We identify certain interesting features and critically discuss their suitability as indicators.

Suggested Citation

  • 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.
  • Handle: RePEc:arx:papers:2107.09048
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    Cited by:

    1. 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.
    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. 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.
    4. 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.
    5. Tobias Wand & Martin He{ss}ler & Oliver Kamps, 2023. "Memory Effects, Multiple Time Scales and Local Stability in Langevin Models of the S&P500 Market Correlation," Papers 2307.12744, arXiv.org.

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