Completing the Market: Generating Shadow CDS Spreads by Machine Learning

Author/Editor:

Nan Hu ; Jian Li ; Alexis Meyer-Cirkel

Publication Date:

December 27, 2019

Electronic Access:

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Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

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. 19/292

Subject:

English

Publication Date:

December 27, 2019

ISBN/ISSN:

9781513524085/1018-5941

Stock No:

WPIEA2019292

Price:

$18.00 (Academic Rate:$18.00)

Format:

Paper

Pages:

37

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