IFRS 9 Expected Loss: A Model Proposal for Estimating the Probability of Default for non-rated companies

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 ca...

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Authors: Delgado-Vaquero, David, Morales-Díaz, José, Zamora-Ramírez, Constancio
Format: article
Publication Date:2020
Country:España
Institution:Universidad de Murcia
Repository:DIGITUM. Depósito Digital Institucional de la Universidad de Murcia
OAI Identifier:oai:digitum.um.es:10201/94542
Online Access:https://doi.org/10.6018/rcsar.370951
http://hdl.handle.net/10201/94542
Access Level:Open access
Keyword:IFRS 9
Impairment of Financial Assets
Probability of Default
Credit rating
Deterioro de Activos Financieros
Probabilidad de Quiebra
Rating Crediticio
CDU::6 - Ciencias aplicadas::65 - Gestión y organización. Administración y dirección de empresas. Publicidad. Relaciones públicas. Medios de comunicación de masas
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spelling IFRS 9 Expected Loss: A Model Proposal for Estimating the Probability of Default for non-rated companiesPérdida prevista según la NIIF 9: una propuesta de modelo para la estimación de la probabilidad de impago en las empresas sin ratingDelgado-Vaquero, DavidMorales-Díaz, JoséZamora-Ramírez, ConstancioIFRS 9Impairment of Financial AssetsProbability of DefaultCredit ratingDeterioro de Activos FinancierosProbabilidad de QuiebraRating CrediticioCDU::6 - Ciencias aplicadas::65 - Gestión y organización. Administración y dirección de empresas. Publicidad. Relaciones públicas. Medios de comunicación de masasUnder 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 resultsBajo el modelo de provisiones por riesgo de crédito de la NIIF 9, las empresas deben estimar una Probabilidad de Default o quiebra (PD) para todos los activos financieros (y otros elementos) no valorados a valor razonable con cambios en la cuenta de resultados. Existen varias metodologías para estimar dicha PD utilizando información histórica o de mercado. No obstante, en algunos casos las empresas no disponen de información histórica o de mercado acerca de una contraparte. Para estos casos proponemos un modelo denominado Financial Ratios Scoring (FRS), a través del cual la entidad puede obtener un rating interno de la contraparte como primer paso para estimar la PD. El modelo se diferencia de otros modelos recientes en varios aspectos como, por ejemplo, el tamaño de la base de datos o el hecho de que se enfoca en empresas sin rating. Se basa en dar una puntuación a la contraparte en función de sus ratios financieros clave. La puntuación sitúa a la empresa en un percentil dentro de una distribución del sector previamente construida utilizando empresas con rating oficial u ofrecido por vendors. Hemos analizado la fiabilidad del modelo calculando el rating interno para empresas con rating oficial y hemos comparado el rating interno con el oficial, obteniendo resultados positivos202020202020info:eu-repo/semantics/articleapplication/pdf17application/pdfhttps://doi.org/10.6018/rcsar.370951http://hdl.handle.net/10201/94542reponame:DIGITUM. Depósito Digital Institucional de la Universidad de Murciainstname:Universidad de MurciaInglésRevista de Contabilidad - Spanish Accounting Review V. 23, N. 2, 2020info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/oai:digitum.um.es:10201/945422026-05-27T12:40:41Z
dc.title.none.fl_str_mv IFRS 9 Expected Loss: A Model Proposal for Estimating the Probability of Default for non-rated companies
Pérdida prevista según la NIIF 9: una propuesta de modelo para la estimación de la probabilidad de impago en las empresas sin rating
title IFRS 9 Expected Loss: A Model Proposal for Estimating the Probability of Default for non-rated companies
spellingShingle IFRS 9 Expected Loss: A Model Proposal for Estimating the Probability of Default for non-rated companies
Delgado-Vaquero, David
IFRS 9
Impairment of Financial Assets
Probability of Default
Credit rating
Deterioro de Activos Financieros
Probabilidad de Quiebra
Rating Crediticio
CDU::6 - Ciencias aplicadas::65 - Gestión y organización. Administración y dirección de empresas. Publicidad. Relaciones públicas. Medios de comunicación de masas
title_short IFRS 9 Expected Loss: A Model Proposal for Estimating the Probability of Default for non-rated companies
title_full IFRS 9 Expected Loss: A Model Proposal for Estimating the Probability of Default for non-rated companies
title_fullStr IFRS 9 Expected Loss: A Model Proposal for Estimating the Probability of Default for non-rated companies
title_full_unstemmed IFRS 9 Expected Loss: A Model Proposal for Estimating the Probability of Default for non-rated companies
title_sort IFRS 9 Expected Loss: A Model Proposal for Estimating the Probability of Default for non-rated companies
dc.creator.none.fl_str_mv Delgado-Vaquero, David
Morales-Díaz, José
Zamora-Ramírez, Constancio
author Delgado-Vaquero, David
author_facet Delgado-Vaquero, David
Morales-Díaz, José
Zamora-Ramírez, Constancio
author_role author
author2 Morales-Díaz, José
Zamora-Ramírez, Constancio
author2_role author
author
dc.subject.none.fl_str_mv IFRS 9
Impairment of Financial Assets
Probability of Default
Credit rating
Deterioro de Activos Financieros
Probabilidad de Quiebra
Rating Crediticio
CDU::6 - Ciencias aplicadas::65 - Gestión y organización. Administración y dirección de empresas. Publicidad. Relaciones públicas. Medios de comunicación de masas
topic IFRS 9
Impairment of Financial Assets
Probability of Default
Credit rating
Deterioro de Activos Financieros
Probabilidad de Quiebra
Rating Crediticio
CDU::6 - Ciencias aplicadas::65 - Gestión y organización. Administración y dirección de empresas. Publicidad. Relaciones públicas. Medios de comunicación de masas
description 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
publishDate 2020
dc.date.none.fl_str_mv 2020
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://doi.org/10.6018/rcsar.370951
http://hdl.handle.net/10201/94542
url https://doi.org/10.6018/rcsar.370951
http://hdl.handle.net/10201/94542
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Revista de Contabilidad - Spanish Accounting Review V. 23, N. 2, 2020
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.format.none.fl_str_mv application/pdf
17
application/pdf
dc.source.none.fl_str_mv reponame:DIGITUM. Depósito Digital Institucional de la Universidad de Murcia
instname:Universidad de Murcia
instname_str Universidad de Murcia
reponame_str DIGITUM. Depósito Digital Institucional de la Universidad de Murcia
collection DIGITUM. Depósito Digital Institucional de la Universidad de Murcia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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