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Credit Spread Approximation and Improvement using Random Forest Regression

Abstract : Credit Default Swap (CDS) levels provide a market appreciation of companies’ default risk. These derivatives are not always available, creating a need for CDS approximations. This paper offers a simple, global and transparent CDS structural approximation, which contrasts with more complex and proprietary approximations currently in use. This Equity-to-Credit formula (E2C), inspired by CreditGrades, obtains better CDS approximations, according to empirical analyses based on a large sample spanning 2016-2018. A random forest regression run with this E2C formula and selected additional financial data results in an 87.3% out-of-sample accuracy in CDS approximations. The transparency property of this algorithm confirms the predominance of the E2C estimate, and the impact of companies’ debt rating and size, in predicting their CDS.
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Soumis le : mardi 5 mars 2019 - 08:54:51
Dernière modification le : mardi 30 août 2022 - 17:14:20


  • HAL Id : hal-02057019, version 1



Mathieu Mercadier, Jean-Pierre Lardy. Credit Spread Approximation and Improvement using Random Forest Regression. European Journal of Operational Research, 2019, 277 (1), pp.351-365. ⟨hal-02057019⟩



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