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Recovery, quantified: How we built a dataset of loan recovery estimates

Author

Listed:
  • Jaime Leyva
  • Tiago Pinheiro

Abstract

This paper estimates recovery parameters for corporate loans in Portugal using granular data from Banco de Portugal’s Credit Register. To correct for sample selection bias present in recovery databases, it models jointly recoveries and the time to recovery. Covering the period from 2009 to 2024, the dataset provides monthly estimates of expected recovery, uncertainty, and time to recovery. These estimates are generally stable, with greater variability after September 2018 due to a more comprehensive loan characterization and improved data granularity. Recovery parameter estimates can be used in applications such as credit risk monitoring, loan pricing, and regulatory capital estimation. The methodology can be applied to similar credit registers.

Suggested Citation

  • Jaime Leyva & Tiago Pinheiro, 2025. "Recovery, quantified: How we built a dataset of loan recovery estimates," Working Papers w202523, Banco de Portugal, Economics and Research Department.
    • Handle: RePEc:ptu:wpaper:w202523
    as

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    File URL: https://www.bportugal.pt/sites/default/files/documents/2025-12/WP202523.pdf
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    References listed on IDEAS

    as
    1. Bellotti, Tony & Crook, Jonathan, 2012. "Loss given default models incorporating macroeconomic variables for credit cards," International Journal of Forecasting, Elsevier, vol. 28(1), pages 171-182.
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