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Global Uncertainty

Author

Listed:
  • Giovanni Caggiano

    (Monash University and University of Padova)

  • Efrem Castelnuovo

    (University of Padova)

Abstract

We estimate a novel measure of global financial uncertainty (GFU) with a dynamic factor framework that jointly models global, regional, and country-specific factors. We quantify the impact of GFU shocks on global output with a VAR analysis that achieves set-identification via a combination of narrative, sign, ratio, and correlation restrictions. We find that the world output loss that materialized during the great recession would have been 13% lower in absence of GFU shocks. We also unveil the existence of a global finance uncertainty multiplier: the more global financial conditions deteriorate after a GFU shock, the larger the world output contraction is.

Suggested Citation

  • Giovanni Caggiano & Efrem Castelnuovo, 2021. "Global Uncertainty," Monash Economics Working Papers 2021-12, Monash University, Department of Economics.
    • Handle: RePEc:mos:moswps:2021-12
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    References listed on IDEAS

    as
    1. Baumeister, Christiane & Hamilton, James D., 2020. "Drawing conclusions from structural vector autoregressions identified on the basis of sign restrictions," Journal of International Money and Finance, Elsevier, vol. 109(C).
    2. Marta Bańbura & Michele Modugno, 2014. "Maximum Likelihood Estimation Of Factor Models On Datasets With Arbitrary Pattern Of Missing Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(1), pages 133-160, January.
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    Cited by:

    1. Claveria, Oscar, 2022. "Global economic uncertainty and suicide: Worldwide evidence," Social Science & Medicine, Elsevier, vol. 305(C).
    2. Oscar Claveria, 2021. "Disagreement on expectations: firms versus consumers," SN Business & Economics, Springer, vol. 1(12), pages 1-23, December.

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    More about this item

    Keywords

    Global Financial Uncertainty; dynamic hierarchical factor model; structural VAR; world output loss; global Önance uncertainty multiplier.;
    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

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