Asset classification under the IFRS 9 framework for the construction of a banking investment portfolio
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10071/22488 |
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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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.Wiley2021-04-26T10:36:46Z2021-01-01T00:00:00Z20212021-04-26T11:36:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10071/22488eng0969-601610.1111/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:RCAAP2023-11-09T17:46:14Zoai:repositorio.iscte-iul.pt:10071/22488Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:22:12.463173Repositório 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 |
2021 |
dc.date.none.fl_str_mv |
2021-04-26T10:36:46Z 2021-01-01T00:00:00Z 2021 2021-04-26T11:36:00Z |
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/10071/22488 |
url |
http://hdl.handle.net/10071/22488 |
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 |
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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1799134783472664576 |