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Identification of Global and Local Shocks in International Financial Markets via General Dynamic Factor Models

Author

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  • Matteo Barigozzi
  • Marc Hallin
  • Stefano Soccorsi

Abstract

We employ a two-stage general dynamic factor model to analyze co-movements between returns and between volatilities of stocks from the U.S., European, and Japanese financial markets. We find two common shocks driving the dynamics of volatilities—one global shock and one United States–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

  • Matteo Barigozzi & Marc Hallin & Stefano Soccorsi, 2019. "Identification of Global and Local Shocks in International Financial Markets via General Dynamic Factor Models," Journal of Financial Econometrics, Oxford University Press, vol. 17(3), pages 462-494.
  • Handle: RePEc:oup:jfinec:v:17:y:2019:i:3:p:462-494.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nby006
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Berger Tino & Hienzsch Sebastian, 2025. "Which Global Cycle? A Stochastic Factor Selection Approach for Global Macro-Financial Cycles," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 29(5), pages 541-559.
    5. 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.
    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. 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.
    8. Niţoi, Mihai & Pochea, Maria Miruna, 2022. "The nexus between bank connectedness and investors’ sentiment," Finance Research Letters, Elsevier, vol. 44(C).
    9. 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.
    10. Mahdi Ghaemi Asl & David Roubaud, 2024. "Asymmetric interactions among cutting-edge technologies and pioneering conventional and Islamic cryptocurrencies: fresh evidence from intra-day-based good and bad volatilities," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-49, December.
    11. 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.
    12. 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.
    13. Linton, Oliver B. & Tang, Haihan & Wu, Jianbin, 2025. "A large confirmatory dynamic factor model for stock market returns in different time zones," Journal of Econometrics, Elsevier, vol. 249(PB).

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    Keywords

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    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
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • G01 - Financial Economics - - General - - - Financial Crises
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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