IFRS 9 Expected LossA Model Proposal for Estimating the Probability of Default for non-rated companies

  1. Delgado-Vaquero, David 1
  2. Morales-Díaz, José 2
  3. Zamora-Ramírez, Constancio 3
  1. 1 EY Spain
  2. 2 Universidad Complutense de Madrid
    info

    Universidad Complutense de Madrid

    Madrid, España

    ROR 02p0gd045

  3. 3 Universidad de Sevilla
    info

    Universidad de Sevilla

    Sevilla, España

    ROR https://ror.org/03yxnpp24

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Journal:
Revista de contabilidad = Spanish accounting review: [RC-SAR]

ISSN: 1138-4891

Year of publication: 2020

Volume: 23

Issue: 2

Pages: 180-196

Type: Article

DOI: 10.6018/RCSAR.370951 DIALNET GOOGLE SCHOLAR lock_openDIGITUM editor

More publications in: Revista de contabilidad = Spanish accounting review: [RC-SAR]

Abstract

Under the IFRS 9 impairment model, entities must estimate the PD (Probability of Default) for all financial assets (and other elements) not measured at fair value through profit or loss. There are several methodologies for estimating this PD from market or historical information. However, in some cases entities do not possess market or historical information concerning a counterparty. For such cases, we propose a model called Financial Ratios Scoring (FRS), by means of which an entity can obtain a “shadow rating” for a counterparty as a first step in estimating the PD. The model differentiates from other recent models in several aspects, such as the size of the database and the fact that it is focused on non-rated companies, for example. It is based on scoring the counterparty according to its key financial ratios. The score will place the counterparty on a percentile within a previously constructed sector distribution using companies with a credit rating published by rating agencies or financial vendors. We have tested the model reliability by calculating the internal credit rating of several companies (which have an official/quoted credit rating), and by comparing the rating obtained with the official one, and obtained positive results.

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