IFRS 9 Expected Loss: A Model Proposal for Estimating the Probability of Default for Non-Rated Companies

33 Pages Posted: 22 May 2019

Date Written: April 2, 2019

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.

Keywords: IFRS 9, Impairment of Financial Assets, Probability of Default, Credit Rating

JEL Classification: C13, G33, C63, M41

Suggested Citation

Delgado-Vaquero, David and Morales-Díaz, José and Zamora-Ramírez, Constancio, IFRS 9 Expected Loss: A Model Proposal for Estimating the Probability of Default for Non-Rated Companies (April 2, 2019). Available at SSRN: https://ssrn.com/abstract=3364451 or http://dx.doi.org/10.2139/ssrn.3364451

José Morales-Díaz

Universidad Complutense ( email )

Campus de Somosaguas
Pozuelo de Alarcón, Madrid 28223
Spain

Constancio Zamora-Ramírez (Contact Author)

University of Seville ( email )

Facultad de Economicas
Ramon y Cajal 1
Sevilla, Sevilla 41018
Spain

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