Credit spread approximation and improvement using random forest regression

TL;DR

This paper introduces a transparent and effective method for approximating CDS levels using a novel E2C formula combined with random forest regression, achieving high accuracy and revealing key financial predictors.

Contribution

It presents a new simple, transparent CDS approximation formula and demonstrates its enhancement with random forest regression for improved accuracy.

Findings

87.3% out-of-sample accuracy in CDS approximation

E2C formula outperforms complex proprietary methods

Debt rating and size significantly influence CDS predictions

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.