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Forecasting Bank Failures in a Data-Rich Environment

Author

Listed:
  • Jean-Armand Gnagne

    (Laval University)

  • Kevin Moran

    (Laval University)

Abstract

This paper develops a monitoring and forecasting model for the aggregate monthly number of commercial bank failures in the U.S. We extract key sectoral predictors from the large set of macroeconomic variables proposed by McCracken and Ng (2016) and incorporate them in a hurdle negative binomial (HNB) model to predict the number of monthly commercial bank failures. Our in-sample analysis uncovers a significant and robust relationship between the predictor related to the housing sector and the occurrence of bank failures, suggesting the importance of the link between developments in that sector and banking vulnerabilities. Out-of-sample exercices, conducted by sequentially re-estimating our HNB model at every step using the real-time vintages of the McCracken and Ng (2016) data, confirm the value of our forecasting approach, which outperforms other alternatives.

Suggested Citation

  • Jean-Armand Gnagne & Kevin Moran, 2020. "Forecasting Bank Failures in a Data-Rich Environment," Working Papers 20-13, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    • Handle: RePEc:bbh:wpaper:20-13
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Financial Regulation; Financial Crises; Factors Models; Diffusion Index Models.;
    All these keywords.

    JEL classification:

    • E60 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - General
    • F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation: Models and Applications
    • F38 - International Economics - - International Finance - - - International Financial Policy: Financial Transactions Tax; Capital Controls
    • G01 - Financial Economics - - General - - - Financial Crises

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