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Clustering in Dynamic Causal Networks as a Measure of Systemic Risk on the Euro Zone

Author

Listed:
  • Monica Billio

    (University of Ca’ Foscari [Venice, Italy])

  • Lorenzo Frattarolo

    (University of Ca’ Foscari [Venice, Italy])

  • Hayette Gatfaoui

    (IESEG - School of Management (LEM), CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - 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

In this paper, we analyze the dynamic relationships between ten stock exchanges of the euro zone using Granger causal networks. Using returns for which we allow the variance to follow a Markov-Switching GARCH or a Changing-Point GARCH, we first show that over different periods, the topology of the network is highly unstable. In particular, over very recent years, dynamic relationships vanish. Then, expanding on this idea, we analyze patterns of information transmission. Using rolling windows to analyze the topologies of the network in terms of clustering, we show that the nodes' state changes continually, and that the system exhibits a high degree of flickering in information transmission. During periods of flickering, the system also exhibits desynchronization in the information transmission process. These periods do precede tipping points or phase transitions on the market, especially before the global financial crisis, and can thus be used as early warnings of phase transitions. To our knowledge, this is the first time that flickering clusters are identified on financial markets, and that flickering is related to phase transitions.

Suggested Citation

  • Monica Billio & Lorenzo Frattarolo & Hayette Gatfaoui & Philippe de Peretti, 2016. "Clustering in Dynamic Causal Networks as a Measure of Systemic Risk on the Euro Zone," Post-Print halshs-01339826, HAL.
    • Handle: RePEc:hal:journl:halshs-01339826
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01339826v2
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    References listed on IDEAS

    as
    1. Bauwens, Luc & Dufays, Arnaud & Rombouts, Jeroen V.K., 2014. "Marginal likelihood for Markov-switching and change-point GARCH models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 508-522.
    2. Billio, Monica & Getmansky, Mila & Lo, Andrew W. & Pelizzon, Loriana, 2012. "Econometric measures of connectedness and systemic risk in the finance and insurance sectors," Journal of Financial Economics, Elsevier, vol. 104(3), pages 535-559.
    3. Hong, Yongmiao, 1996. "Testing for independence between two covariance stationary time series," MPRA Paper 108731, University Library of Munich, Germany.
    4. Triacca, Umberto, 1998. "Non-causality: The role of the omitted variables," Economics Letters, Elsevier, vol. 60(3), pages 317-320, September.
    5. Marc Hallin & Abdessamad Saidi, 2005. "Testing Non‐Correlation and Non‐Causality between Multivariate ARMA Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(1), pages 83-105, January.
    6. Dinh Tuan Pham & Roch Roy & Lyne Cédras, 2003. "Tests for non‐correlation of two cointegrated ARMA time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(5), pages 553-577, September.
    7. Douglas Zhou & Yanyang Xiao & Yaoyu Zhang & Zhiqin Xu & David Cai, 2014. "Granger Causality Network Reconstruction of Conductance-Based Integrate-and-Fire Neuronal Systems," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-17, February.
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    Cited by:

    1. Franch, Fabio & Nocciola, Luca & Vouldis, Angelos, 2024. "Temporal networks and financial contagion," Journal of Financial Stability, Elsevier, vol. 71(C).
    2. Bertrand Candelon & Laurent Ferrara & Marc Joëts, 2021. "Global financial interconnectedness: a non-linear assessment of the uncertainty channel," Applied Economics, Taylor & Francis Journals, vol. 53(25), pages 2865-2887, May.
    3. Clemente, G.P. & Grassi, R., 2018. "Directed clustering in weighted networks: A new perspective," Chaos, Solitons & Fractals, Elsevier, vol. 107(C), pages 26-38.

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    More about this item

    Keywords

    Causal Network; Topology; Clustering; Flickering; Desynchronisation; Phase transitions;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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