Completing the Market: Generating Shadow CDS Spreads by Machine Learning

Abstract

We compared the predictive performance of a series of machine learning and traditional methods for monthly CDS spreads, using firms' accounting-based, market-based and macroeconomics variables for a time period of 2006 to 2016. We find that ensemble machine learning methods (Bagging, Gradient Boosting and Random Forest) strongly outperform other estimators, and Bagging particularly stands out in terms of accuracy. Traditional credit risk models using OLS techniques have the lowest out-of-sample prediction accuracy. The results suggest that the non-linear machine learning methods, especially the ensemble methods, add considerable value to existent credit risk prediction accuracy and enable CDS shadow pricing for companies missing those securities.

Keywords: Financial crises, Economic conditions, Financial markets, Financial systems, Credit risk, Credit default swaps, Prediction, Machine Learning methods, WP, CDS, test sample, test set, nowcasting, linear regression

JEL Classification: C10, G1, G12, C52, C53, E01, F16, E52, G21