Novosibirsk, Novosibirsk, Russian Federation
Institute of Economics and Industrial Engineering
Novosibirsk State Technical University
Novosibirsk, Novosibirsk, Russian Federation
JEL G33 Bankruptcy • Liquidation
JEL C51 Model Construction and Estimation
JEL C58 Financial Econometrics
The bank’s loss given default (LGD) – the proportion of losses incurred when a borrower defaults – is a critically important metric. Without its accurate forecasting, it is impossible to correctly assess the level of credit risk, adequately form reserves for expected credit losses, or calculate the sufficient amount of capital required to safeguard the institution’s financial stability against unexpected credit losses arising from loan defaults. This metric reflects the proportion of unpaid debt relative to its total amount and directly influences the level of provisioning, regulatory requirements, and strategic planning of banking operations. Predicting the losses given default requires considering several specific data properties, including multimodality, asymmetric distribution, constraints within the interval [0,1], and nonlinear relationships with explanatory factors. This article reviews studies focused on modeling the share of default losses within the credit risk assessment. Model quality metrics are discussed based on four key criteria: accuracy, discriminative ability, stability, and interpretability. The article provides with recommendations for model development and implementation. Hybrid models that combine the advantages of parametric and non-parametric approaches, as well as interpretable non-parametric models, are becoming increasingly popular in credit risk assessment due to their ability to offer both predictive accuracy and interpretability. Nevertheless, parametric models remain the base analytical tool and benchmark against which the performance gains of more sophisticated models can be compared.
credit risk, Loss Given Default, multimodal data distribution, interpretability, non-parametric models, hybrid models
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