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Brito, R. P. & Judice, P. (2022). Asset classification under the IFRS 9 framework for the construction of a banking investment portfolio. International Transactions of Operations Research. 29 (4), 2618-2648
R. P. Brito and P. M. Judice, "Asset classification under the IFRS 9 framework for the construction of a banking investment portfolio", in Int. Transactions of Operations Research, vol. 29, no. 4, pp. 2618-2648, 2022
@article{brito2022_1724525497570, author = "Brito, R. P. and Judice, P.", title = "Asset classification under the IFRS 9 framework for the construction of a banking investment portfolio", journal = "International Transactions of Operations Research", year = "2022", volume = "29", number = "4", doi = "10.1111/itor.12976", pages = "2618-2648", url = "https://onlinelibrary.wiley.com/journal/14753995" }
TY - JOUR TI - Asset classification under the IFRS 9 framework for the construction of a banking investment portfolio T2 - International Transactions of Operations Research VL - 29 IS - 4 AU - Brito, R. P. AU - Judice, P. PY - 2022 SP - 2618-2648 SN - 0969-6016 DO - 10.1111/itor.12976 UR - https://onlinelibrary.wiley.com/journal/14753995 AB - 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. ER -