Credit Spread Approximation and Improvement using Random Forest Regression

Mathieu Mercadier and Jean-Pierre Lardy
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Jean-Pierre Lardy: JPLC SASU

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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.

Keywords: Risk Analysis; Finance; Structural Model; Random Forests; Credit Default Swaps (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (10)

Published in European Journal of Operational Research, 2019, 277 (1), pp.351-365

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Related works:
Working Paper: Credit spread approximation and improvement using random forest regression (2021)
Journal Article: Credit spread approximation and improvement using random forest regression (2019)
Working Paper: Credit spread approximation and improvement using random forest regression (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02057019

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