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Rating Trajectories and Credit Risk Migration: Evidence for SMEs

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Listed:
  • Camilla Ferretti

    (DISCE, Università Cattolica)

  • Giampaolo Gabbi

    (DISAG, Università di Siena)

  • Piero Ganugi

    (DIA, Università di Parma)

  • Pietro Vozzella

    (DISAG, Università di Siena)

Abstract

The misestimation of rating transition probabilities may lead banks to lend money incoherently with borrowers’ default trajectory, causing both a deterioration in asset quality and higher system distress. Applying a Mover-Stayer model to determine the migration risk of small and medium enterprises, we find that banks are overestimating their credit risk resulting in excessive regulatory capital. This has important macroeconomic implications due to the fact that holding a large capital buffer is costly for banks and this in turn influences their ability to lend in the wider economy. This conclusion is particularly true during economic downturns with the consequence of exacerbating the cyclicality in risk capital that therefore acts to aggravate economic conditions further. We also explain part of the misevaluation of borrowers and the actual relevant weight of nonperforming loans within banking portfolios: prudential prescriptions cannot be considered as effective as expected by regulators who have designed the “new” regulation in response to the most recent crisis.The Mover-Stayers approach helps to reduce calculation inaccuracy when analyzing the historical movements of borrowers’ ratings and, consequently improves the efficacy of the resource allocation process and banking industry stability.

Suggested Citation

  • Camilla Ferretti & Giampaolo Gabbi & Piero Ganugi & Pietro Vozzella, 2016. "Rating Trajectories and Credit Risk Migration: Evidence for SMEs," DISCE - Quaderni del Dipartimento di Scienze Economiche e Sociali dises1615, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
  • Handle: RePEc:ctc:serie2:dises1615
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    References listed on IDEAS

    as
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    Keywords

    credit risk; Markov chains; absorbing state; rating migration;
    All these keywords.

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