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Identification of global and local shocks in international financial markets via general dynamic factor models

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  • Barigozzi, Matteo
  • Hallin, Marc
  • Soccorsi, Stefano

Abstract

We employ a two-stage general dynamic factor model to analyze co-movements between returns and between volatilities of stocks from the US, European, and Japanese financial markets. We find two common shocks driving the dynamics of volatilities – one global shock and one US-European shock – and four local shocks driving returns, but no global one. Co-movements in returns and volatilities increased considerably in the period 2007-2012 associated with the Great Financial Crisis and the European Sovereign Debt Crisis. We interpret this finding as the sign of a surge, during crises, of interdependencies across markets, as opposed to contagion. Finally, we introduce a new method for structural analysis in general dynamic factor models which is applied to the identification of volatility shocks via natural timing assumptions. The global shock has homogeneous dynamic effects within each individual market but more heterogeneous effects across them, and is useful for predicting aggregate realized volatilities.

Suggested Citation

  • Barigozzi, Matteo & Hallin, Marc & Soccorsi, Stefano, 2019. "Identification of global and local shocks in international financial markets via general dynamic factor models," LSE Research Online Documents on Economics 86932, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:86932
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    References listed on IDEAS

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    1. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
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    Cited by:

    1. Riccardo Borghi & Eric Hillebrand & Jakob Mikkelsen & Giovanni Urga, 2018. "The dynamics of factor loadings in the cross-section of returns," CREATES Research Papers 2018-38, Department of Economics and Business Economics, Aarhus University.
    2. Carlos Cesar Trucios-Maza & João H. G Mazzeu & Luis K. Hotta & Pedro L. Valls Pereira & Marc Hallin, 2019. "On the robustness of the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Working Papers ECARES 2019-32, ULB -- Universite Libre de Bruxelles.
    3. Giovannelli, Alessandro & Massacci, Daniele & Soccorsi, Stefano, 2021. "Forecasting stock returns with large dimensional factor models," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 252-269.
    4. Matteo Barigozzi & Marc Hallin & Stefano Soccorsi, 2019. "Time-Varying General Dynamic Factor Models and the Measurement of Financial Connectedness," Working Papers ECARES 2019-09, ULB -- Universite Libre de Bruxelles.
    5. Lucchetti, Riccardo & Venetis, Ioannis A., 2020. "A replication of "A quasi-maximum likelihood approach for large, approximate dynamic factor models" (Review of Economics and Statistics, 2012)," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 14, pages 1-14.
    6. Barigozzi, Matteo & Hallin, Marc & Soccorsi, Stefano & von Sachs, Rainer, 2021. "Time-varying general dynamic factor models and the measurement of financial connectedness," Journal of Econometrics, Elsevier, vol. 222(1), pages 324-343.
    7. Thomas Lux & Duc Thi Luu & Boyan Yanovski, 2020. "An analysis of systemic risk in worldwide economic sentiment indices," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 47(4), pages 909-928, November.
    8. Trucíos, Carlos & Mazzeu, João H.G. & Hotta, Luiz K. & Valls Pereira, Pedro L. & Hallin, Marc, 2021. "Robustness and the general dynamic factor model with infinite-dimensional space: Identification, estimation, and forecasting," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1520-1534.
    9. Barigozzi, Matteo & Hallin, Marc, 2020. "Generalized dynamic factor models and volatilities: Consistency, rates, and prediction intervals," Journal of Econometrics, Elsevier, vol. 216(1), pages 4-34.
    10. Niţoi, Mihai & Pochea, Maria Miruna, 2022. "The nexus between bank connectedness and investors’ sentiment," Finance Research Letters, Elsevier, vol. 44(C).

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    More about this item

    Keywords

    dynamic factor models; volatility; financial crises; contagion; interdependence;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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