Asset classification under the IFRS 9 framework for the construction of a banking investment portfolio

Detalhes bibliográficos
Autor(a) principal: Brito, R. P.
Data de Publicação: 2022
Outros Autores: Judice, P.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10071/22596
Resumo: Under the International Financial Reporting Standard 9 framework, we analyze the trade-off of classifying a financial asset at amortized cost versus at fair value. Defining an impairment model and based on historical (2003–2019) data for the 10-year Portuguese Government bonds, we analyze the annual performance (income/comprehensive income) of different investment allocations. Setting as objectives the maximization of the income and the minimization of the semivariance of the comprehensive income, we suggest a biobjective model in order to find efficient allocations. Given the nonsmoothness of the semivariance function, we compute the solution of the suggested model by means of a multiobjective derivative-free algorithm. Assuming that the yields and funding rates follow a correlated mean-reverting process and that the bonds’ rating dynamics are described by an ordinal response model, we show a possible approach to mitigate the estimation error ingrained in the proposed biobjective stochastic model. Finally, we assess the out-of-sample performance of some of the suggested efficient allocations.
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spelling Asset classification under the IFRS 9 framework for the construction of a banking investment portfolioAsset classificationBacktestingIFRS 9Derivative-free optimizationSensitivity analysisStochastic simulationUnder the International Financial Reporting Standard 9 framework, we analyze the trade-off of classifying a financial asset at amortized cost versus at fair value. Defining an impairment model and based on historical (2003–2019) data for the 10-year Portuguese Government bonds, we analyze the annual performance (income/comprehensive income) of different investment allocations. Setting as objectives the maximization of the income and the minimization of the semivariance of the comprehensive income, we suggest a biobjective model in order to find efficient allocations. Given the nonsmoothness of the semivariance function, we compute the solution of the suggested model by means of a multiobjective derivative-free algorithm. Assuming that the yields and funding rates follow a correlated mean-reverting process and that the bonds’ rating dynamics are described by an ordinal response model, we show a possible approach to mitigate the estimation error ingrained in the proposed biobjective stochastic model. Finally, we assess the out-of-sample performance of some of the suggested efficient allocations.Wiley2023-04-11T00:00:00Z2022-01-01T00:00:00Z20222022-04-07T13:51:01Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/22596eng0969-601610.1111/itor.12976Brito, R. P.Judice, P.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-09T17:53:40Zoai:repositorio.iscte-iul.pt:10071/22596Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:26:56.882355Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Asset classification under the IFRS 9 framework for the construction of a banking investment portfolio
title Asset classification under the IFRS 9 framework for the construction of a banking investment portfolio
spellingShingle Asset classification under the IFRS 9 framework for the construction of a banking investment portfolio
Brito, R. P.
Asset classification
Backtesting
IFRS 9
Derivative-free optimization
Sensitivity analysis
Stochastic simulation
title_short Asset classification under the IFRS 9 framework for the construction of a banking investment portfolio
title_full Asset classification under the IFRS 9 framework for the construction of a banking investment portfolio
title_fullStr Asset classification under the IFRS 9 framework for the construction of a banking investment portfolio
title_full_unstemmed Asset classification under the IFRS 9 framework for the construction of a banking investment portfolio
title_sort Asset classification under the IFRS 9 framework for the construction of a banking investment portfolio
author Brito, R. P.
author_facet Brito, R. P.
Judice, P.
author_role author
author2 Judice, P.
author2_role author
dc.contributor.author.fl_str_mv Brito, R. P.
Judice, P.
dc.subject.por.fl_str_mv Asset classification
Backtesting
IFRS 9
Derivative-free optimization
Sensitivity analysis
Stochastic simulation
topic Asset classification
Backtesting
IFRS 9
Derivative-free optimization
Sensitivity analysis
Stochastic simulation
description Under the International Financial Reporting Standard 9 framework, we analyze the trade-off of classifying a financial asset at amortized cost versus at fair value. Defining an impairment model and based on historical (2003–2019) data for the 10-year Portuguese Government bonds, we analyze the annual performance (income/comprehensive income) of different investment allocations. Setting as objectives the maximization of the income and the minimization of the semivariance of the comprehensive income, we suggest a biobjective model in order to find efficient allocations. Given the nonsmoothness of the semivariance function, we compute the solution of the suggested model by means of a multiobjective derivative-free algorithm. Assuming that the yields and funding rates follow a correlated mean-reverting process and that the bonds’ rating dynamics are described by an ordinal response model, we show a possible approach to mitigate the estimation error ingrained in the proposed biobjective stochastic model. Finally, we assess the out-of-sample performance of some of the suggested efficient allocations.
publishDate 2022
dc.date.none.fl_str_mv 2022-01-01T00:00:00Z
2022
2022-04-07T13:51:01Z
2023-04-11T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10071/22596
url http://hdl.handle.net/10071/22596
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0969-6016
10.1111/itor.12976
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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