Completing the Market: Generating Shadow CDS Spreads by Machine Learning
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Summary:
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.
Series:
Working Paper No. 2019/292
Subject:
Credit default swap Credit ratings Credit risk Financial markets Financial regulation and supervision Machine learning Money Stock markets Technology
English
Publication Date:
December 27, 2019
ISBN/ISSN:
9781513524085/1018-5941
Stock No:
WPIEA2019292
Pages:
37
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