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

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Abstract
In this paper, we analyze the dynamic relationships between ten stock exchanges of the euro zone using Granger causal networks. Considering returns for which we allow the variance to follow a Markov-Switching GARCH or a Changing-Point GARCH process, we first show that over different periods, the topology of the network is highly unstable. In particular dynamic relationships vanish over very recent years. Then, expanding on this idea, we analyze patterns of information transmission within the network. Using rolling windows to study networks' topology in terms of information clustering, we find that the nodes' state changes continually. Moreover, the system exhibits periods of flickering in information tranmission. During these 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. To our knowledge, this is the first time that flickering in information transmission is 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," Documents de travail du Centre d'Economie de la Sorbonne 16046r, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Sep 2016.
    • Handle: RePEc:mse:cesdoc:16046r
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    References listed on IDEAS

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    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. 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.
    2. Clemente, G.P. & Grassi, R., 2018. "Directed clustering in weighted networks: A new perspective," Chaos, Solitons & Fractals, Elsevier, vol. 107(C), pages 26-38.
    3. Franch, Fabio & Nocciola, Luca & Vouldis, Angelos, 2024. "Temporal networks and financial contagion," Journal of Financial Stability, Elsevier, vol. 71(C).
    4. Atasoy, Burak Sencer & Özkan, İbrahim, 2024. "Correlation meets causality: A holistic measure of financial contagion," Finance Research Letters, Elsevier, vol. 65(C).

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    More about this item

    Keywords

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

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • G1 - Financial Economics - - General Financial Markets

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