Credit portfolio optimization: A multi-objective genetic algorithm approach

The algorithm for optimization of a credit portfolio has not been fully demonstrated. This paper fills the gap in the literature by presenting a general approach for optimizing a credit portfolio by minimizing the default risk of the entire portfolio. Default risk is measured with quadratic weightin...

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Veröffentlicht in:	Borsa Istanbul Review Jg. 22; H. 1; S. 69 - 76
Hauptverfasser:	Wang, Zhi, Zhang, Xuan, Zhang, ZheKai, Sheng, Dachen
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.01.2022 Elsevier
Schlagworte:	C61 Credit portfolio optimization G11 Heuristic optimization Multi-objective genetic algorithm Multi-objective genetic algorithm Heuristic optimization G11 Credit portfolio optimization C61
ISSN:	2214-8450
Online-Zugang:	Volltext
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Zusammenfassung:	The algorithm for optimization of a credit portfolio has not been fully demonstrated. This paper fills the gap in the literature by presenting a general approach for optimizing a credit portfolio by minimizing the default risk of the entire portfolio. Default risk is measured with quadratic weighting and a matrix containing information about the default intensity of two stocks and the correlation in default between them. The default correlation and the default intensity are represented with a novel bivariate intensity model. A multi-objective genetic algorithm is introduced to optimize a credit portfolio with the purpose of overcoming limitations in the analytical method and improving the efficiency of optimization. The algorithm can be applied to a portfolio's credit risk management, which is particularly crucial for investors and regulars in emerging markets.
ISSN:	2214-8450
DOI:	10.1016/j.bir.2021.01.004