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Clustering Dynamics and Persistence for Financial Multivariate Panel Data

Author

Listed:
  • Igor Custodio João

    (Vrije Universiteit Amsterdam)

  • Andre Lucas

    (Vrije Universiteit Amsterdam)

  • Julia Schaumburg

    (Vrije Universiteit Amsterdam)

Abstract

We introduce a new method for dynamic clustering of panel data with dynamics for cluster location and shape, cluster composition, and for the number of clusters. Whereas current techniques typically result in (economically) too many switches, our method results in economically more meaningful dynamic clustering patterns. It does so by extending standard cross-sectional clustering techniques using shrinkage towards previous cluster means. In this way, the different cross-sections in the panel are tied together, substantially reducing short-lived switches of units between clusters (flickering) and the birth and death of incidental, economically less meaningful clusters. In a Monte Carlo simulation, we study how to set the penalty parameter in a data-driven way. A systemic risk surveillance example for business model classification in the global insurance industry illustrates how the new method works empirically.

Suggested Citation

  • Igor Custodio João & Andre Lucas & Julia Schaumburg, 2021. "Clustering Dynamics and Persistence for Financial Multivariate Panel Data," Tinbergen Institute Discussion Papers 21-040/III, Tinbergen Institute.
  • Handle: RePEc:tin:wpaper:20210040
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    References listed on IDEAS

    as
    1. Stéphane Bonhomme & Elena Manresa, 2015. "Grouped Patterns of Heterogeneity in Panel Data," Econometrica, Econometric Society, vol. 83(3), pages 1147-1184, May.
    2. André Lucas & Julia Schaumburg & Bernd Schwaab, 2019. "Bank Business Models at Zero Interest Rates," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(3), pages 542-555, July.
    3. Custodio João, Igor & Lucas, André & Schaumburg, Julia & Schwaab, Bernd, 2023. "Dynamic clustering of multivariate panel data," Journal of Econometrics, Elsevier, vol. 237(2).
    4. Biener, Christian & Eling, Martin & Jia, Ruo, 2017. "The structure of the global reinsurance market: An analysis of efficiency, scale, and scope," Journal of Banking & Finance, Elsevier, vol. 77(C), pages 213-229.
    5. Leopoldo Catania, 2021. "Dynamic Adaptive Mixture Models with an Application to Volatility and Risk," Journal of Financial Econometrics, Oxford University Press, vol. 19(4), pages 531-564.
    6. Leopoldo Catania, 2016. "Dynamic Adaptive Mixture Models," Papers 1603.01308, arXiv.org, revised Jan 2023.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    dynamic clustering; shrinkage; cluster membership persistence; Silhouette index; insurance;
    All these keywords.

    JEL classification:

    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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