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 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. Copyright (C) 2021, Borsa Istanbul Anonim Sirketi. Production and hosting by Elsevier B.V.

Automated summary

This paper presents a general approach for optimizing a credit portfolio by minimizing the default risk of the entire portfolio. It introduces quadratic weighting and a novel bivariate intensity model to measure default risk, and utilizes a multi-objective genetic algorithm to improve optimization efficiency.