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Estimating Global Bank Network Connectedness

Author

Listed:
  • Mert Demirer

    (MIT)

  • Francis X. Diebold

    (University of Pennsylvania)

  • Laura Liu

    (University of Pennsylvania)

  • Kamil Yilmaz

    (Koc University)

Abstract

We use lasso methods to shrink, select and estimate the network linking the publicly-traded subset of the world's top 150 banks, 2003-2014. We characterize static network connectedness using full-sample estimation and dynamic network connectedness using rolling-window estimation. Statistically, we find that global banking connectedness is clearly linked to bank location, not bank assets. Dynamically, we find that global banking connectedness displays both secular and cyclical variation. The secular variation corresponds to gradual increases/decreases during episodes of gradual increases/decreases in global market integration. The cyclical variation corresponds to sharp increases during crises, involving mostly cross-country, as opposed to within-country, bank linkages.

Suggested Citation

  • Mert Demirer & Francis X. Diebold & Laura Liu & Kamil Yilmaz, 2015. "Estimating Global Bank Network Connectedness," Koç University-TUSIAD Economic Research Forum Working Papers 1512, Koc University-TUSIAD Economic Research Forum.
  • Handle: RePEc:koc:wpaper:1512
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    References listed on IDEAS

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    More about this item

    Keywords

    Systemic risk; connectedness; systemically important financial institutions; vector autoregression; variance decomposition; lasso; elastic net; adaptive lasso; adaptive elastic net.;
    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
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

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