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

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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.
Autores principais:Brito, R. P.
Outros Autores:Judice, P.
Assunto:Asset classification Backtesting IFRS 9 Derivative-free optimization Sensitivity analysis Stochastic simulation
Ano:2022
País:Portugal
Tipo de documento:artigo
Tipo de acesso:acesso aberto
Instituição associada:ISCTE
Idioma:inglês
Origem:Repositório ISCTE
Descrição
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.