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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_1734977849557,
	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  -