Completing the Market: Generating Shadow CDS Spreads by Machine Learning

Nan Hu, Jian Li () and Alexis Meyer-Cirkel

No 2019/292, IMF Working Papers from International Monetary Fund

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: WP; firm; default probability; failure intensity; firm size proxy; Credit default swaps; Prediction; Machine Learning methods; input variable; contracts data; recovery rate; importance probability matrix; credit measure Distance to default; coverage ratio; equity market variable; macroeconomic variable; market perception; machine learning method; Credit default swap; Machine learning; Credit risk; Credit ratings; Stock markets; North America (search for similar items in EconPapers)
Pages: 37
Date: 2019-12-27
New Economics Papers: this item is included in nep-big and nep-cmp
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