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Correlations, Risk and Crisis: From Physiology to Finance

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  • A. N. Gorban
  • E. V. Smirnova
  • T. A. Tyukina

Abstract

We study the dynamics of correlation and variance in systems under the load of environmental factors. A universal effect in ensembles of similar systems under the load of similar factors is described: in crisis, typically, even before obvious symptoms of crisis appear, correlation increases, and, at the same time, variance (and volatility) increases too. This effect is supported by many experiments and observations of groups of humans, mice, trees, grassy plants, and on financial time series. A general approach to the explanation of the effect through dynamics of individual adaptation of similar non-interactive individuals to a similar system of external factors is developed. Qualitatively, this approach follows Selye's idea about adaptation energy.

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  • A. N. Gorban & E. V. Smirnova & T. A. Tyukina, 2009. "Correlations, Risk and Crisis: From Physiology to Finance," Papers 0905.0129, arXiv.org, revised Aug 2010.
    • Handle: RePEc:arx:papers:0905.0129
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    References listed on IDEAS

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    1. Çukur, Sadik & Eryiğit, Mehmet & Eryiğit, Resul, 2007. "Cross correlations in an emerging market financial data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 376(C), pages 555-564.
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    Cited by:

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    2. Giovanna Zimatore & Maria Chiara Gallotta & Matteo Campanella & Piotr H. Skarzynski & Giuseppe Maulucci & Cassandra Serantoni & Marco De Spirito & Davide Curzi & Laura Guidetti & Carlo Baldari & Stavr, 2022. "Detecting Metabolic Thresholds from Nonlinear Analysis of Heart Rate Time Series: A Review," IJERPH, MDPI, vol. 19(19), pages 1-24, October.
    3. Sandler, U., 2017. "S-Lagrangian dynamics of many-body systems and behavior of social groups: Dominance and hierarchy formation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 218-241.
    4. Xing, Kai & Yang, Xiaoguang, 2020. "Predicting default rates by capturing critical transitions in the macroeconomic system," Finance Research Letters, Elsevier, vol. 32(C).
    5. Heiberger, Raphael H., 2018. "Predicting economic growth with stock networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 489(C), pages 102-111.
    6. Giuseppe Orlando & Giovanna Zimatore, 2021. "Recurrence Quantification Analysis of Business Cycles," Dynamic Modeling and Econometrics in Economics and Finance, in: Giuseppe Orlando & Alexander N. Pisarchik & Ruedi Stoop (ed.), Nonlinearities in Economics, chapter 0, pages 269-282, Springer.
    7. Sviatoslav R. Rybnikov & Natalya A. Rybnikova & Boris A. Portnov, 2017. "Public Fears in Ukrainian Society," Psychology and Developing Societies, , vol. 29(1), pages 98-123, March.
    8. Ranjeeni, Kumari, 2014. "Sectoral and industrial performance during a stock market crisis," Economic Systems, Elsevier, vol. 38(2), pages 178-193.
    9. Damasco, Achille & Giuliani, Alessandro, 2017. "A resonance based model of biological evolution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 750-756.
    10. Y. Shi & A. N. Gorban & T. Y. Yang, 2013. "Is it possible to predict long-term success with k-NN? Case Study of four market indices (FTSE100, DAX, HANGSENG, NASDAQ)," Papers 1307.8308, arXiv.org.
    11. Yao, Hongxing & Memon, Bilal Ahmed, 2019. "Network topology of FTSE 100 Index companies: From the perspective of Brexit," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1248-1262.

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