Credit spread approximation and improvement using random forest regression
This provides a more accessible and interpretable method for financial analysts and institutions to estimate default risk, though it is incremental compared to existing proprietary approximations.
The paper tackled the problem of approximating Credit Default Swap (CDS) levels when they are unavailable by developing a simple, transparent Equity-to-Credit (E2C) formula, which achieved an 87.3% out-of-sample accuracy when combined with random forest regression and financial data.
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.