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Monitoring Bank Failures in a Data-Rich Environment

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  • Jean Armand Gnagne
  • Kevin Moran

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 model to predict the number of monthly commercial bank failures. We uncover a strong and robust relationship between the predictor synthesizing housing industry variables and bank failures. This relationship suggests the existence of a link between developments in the housing sector and the vulnerability of commercial banks to non-performing loans increases and asset deterioration. We assess different specifications

Suggested Citation

  • Jean Armand Gnagne & Kevin Moran, 2018. "Monitoring Bank Failures in a Data-Rich Environment," Cahiers de recherche 1815, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
    • Handle: RePEc:lvl:crrecr:1815
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    Cited by:

    1. 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.

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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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