PREPAYMENT AND DEFAULT RISK: A REVIEW

Abstract

Purpose- The main purpose of this article is to make a comprehensive review of existing studies on prepayment and default (competing risk). This review enables to shed light on the main determinant of prepayment and default as well as on methods used to model competing risk. Methodology- A comprehensive review of existing studies/articles. Findings- More recently proposed machine learning methods (Random Survival Forest and Random Competing Risks Forests, as well as the DeepHit model and Dynamic DeepHit model) enable to take into account the complex/no-linear response of prepayment and default to their determinant more efficiently. Conclusion- To model properly/correctly the prepayment and default risks it is important to consider the fact that the exercise of the prepayment option brings an end to the default option, and vice versa. These both risks should be modelled together: competing risk. Furthermore, models/methods accounting the complex/no-linear impact of explanatory variables on prepayment and default risks should be used; such as the Random Survival Forest and Random Competing Risks Forests, as well as the DeepHit model and Dynamic DeepHit model.

Keywords

• Prepayment risk
• default risk
• multinomial logit models
• multi-state model
• random survival forest
• Dynamic DeepHit model

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