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Flickering in Information Spreading Precedes Critical Transitions in Financial Markets

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  • Hayette Gatfaoui

    (IÉSEG School Of Management [Puteaux], CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

  • Philippe de Peretti

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

Abstract

As many complex dynamical systems, financial markets exhibit sudden changes or tipping points that can turn into systemic risk. This paper aims at building and validating a new class of early warning signals of critical transitions. We base our analysis on information spreading patterns in dynamic temporal networks, where nodes are connected by short-term causality. Before a tipping point occurs, we observe flickering in information spreading, as measured by clustering coefficients. Nodes rapidly switch between "being in" and "being out" the information diffusion process. Concurrently, stock markets start to desynchronize. To capture these features, we build two early warning indicators based on the number of regime switches, and on the time between two switches. We divide our data into two sub-samples. Over the first one, using receiver operating curve, we show that we are able to detect a tipping point about one year before it occurs. For instance, our empirical model perfectly predicts the Global Financial Crisis. Over the second sub-sample, used as a robustness check, our two statistical metrics also capture, to a large extent, the 2016 financial turmoil. Our results suggest that our indicators have informational content about a future tipping point, and have therefore strong policy implications.

Suggested Citation

  • Hayette Gatfaoui & Philippe de Peretti, 2019. "Flickering in Information Spreading Precedes Critical Transitions in Financial Markets," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-02098605, HAL.
  • Handle: RePEc:hal:cesptp:hal-02098605
    DOI: 10.1038/s41598-019-42223-9
    Note: View the original document on HAL open archive server: https://hal.science/hal-02098605v1
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    Cited by:

    1. Christophe Chorro & Emmanuelle Jay & Philippe de Peretti & Thibault Soler, 2021. "Frequency causality measures and Vector AutoRegressive (VAR) models: An improved subset selection method suited to parsimonious systems," Post-Print halshs-03216938, HAL.
    2. Christophe Chorro & Emmanuelle Jay & Philippe De Peretti & Thibault Soler, 2021. "Frequency causality measures and Vector AutoRegressive (VAR) models: An improved subset selection method suited to parsimonious systems," Documents de travail du Centre d'Economie de la Sorbonne 21013, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    3. Andrew R. Tilman & Elisabeth H. Krueger & Lisa C. McManus & James R. Watson, 2023. "Maintaining human wellbeing as socio-environmental systems undergo regime shifts," Papers 2309.04578, arXiv.org.
    4. An, Sufang & An, Feng & Gao, Xiangyun & Wang, Anjian, 2023. "Early warning of critical transitions in crude oil price," Energy, Elsevier, vol. 280(C).
    5. Ismail, Mohd Sabri & Noorani, Mohd Salmi Md & Ismail, Munira & Razak, Fatimah Abdul & Alias, Mohd Almie, 2022. "Early warning signals of financial crises using persistent homology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    6. Christophe Chorro & Emmanuelle Jay & Philippe de Peretti & Thibault Soler, 2021. "Frequency causality measures and Vector AutoRegressive (VAR) models: An improved subset selection method suited to parsimonious systems," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-03216938, HAL.
    7. Tilman, Andrew R. & Krueger, Elisabeth H. & McManus, Lisa C. & Watson, James R., 2024. "Maintaining human wellbeing as socio-environmental systems undergo regime shifts," Ecological Economics, Elsevier, vol. 221(C).
    8. Paul Hutchings & Simon Willcock & Kenneth Lynch & Dilshaad Bundhoo & Tim Brewer & Sarah Cooper & Daniel Keech & Sneha Mekala & Prajna Paramita Mishra & Alison Parker & Charlie M. Shackleton & Kongala , 2022. "Understanding rural–urban transitions in the Global South through peri-urban turbulence," Nature Sustainability, Nature, vol. 5(11), pages 924-930, November.
    9. Song, Shijia & Li, Handong, 2025. "Can topological transitions in cryptocurrency systems serve as early warning signals for extreme fluctuations in traditional markets?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 657(C).

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