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|z 9781513524085
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|a 1018-5941
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|a Hu, Nan.
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|a Completing the Market :
|b Generating Shadow CDS Spreads by Machine Learning /
|c Nan Hu, Jian Li, Alexis Meyer-Cirkel.
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|a Washington, D.C. :
|b International Monetary Fund,
|c 2019.
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|a 1 online resource (37 pages)
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|a IMF Working Papers
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|a <strong>Off-Campus Access:</strong> No User ID or Password Required
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|a <strong>On-Campus Access:</strong> No User ID or Password Required
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|a Electronic access restricted to authorized BRAC University faculty, staff and students
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|a 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.
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|a Mode of access: Internet
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|a Li, Jian.
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|a Meyer-Cirkel, Alexis.
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|a IMF Working Papers; Working Paper ;
|v No. 2019/292
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| 856 |
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|z Full text available on IMF
|u http://elibrary.imf.org/view/journals/001/2019/292/001.2019.issue-292-en.xml
|z IMF e-Library
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