Completing the Market: Generating Shadow CDS Spreads by Machine Learning

Download or read book Completing the Market: Generating Shadow CDS Spreads by Machine Learning written by Nan Hu and published by International Monetary Fund. This book was released on 2019-12-27 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.