Completing the Market : Generating Shadow CDS Spreads by Machine Learning /
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 B...
| Հիմնական հեղինակ: | |
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| Այլ հեղինակներ: | , |
| Ձևաչափ: | Ամսագիր |
| Լեզու: | English |
| Հրապարակվել է: |
Washington, D.C. :
International Monetary Fund,
2019.
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| Շարք: | IMF Working Papers; Working Paper ;
No. 2019/292 |
| Առցանց հասանելիություն: | Full text available on IMF |
| Ամփոփում: | 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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| Նյութի նկարագրություն: | <strong>Off-Campus Access:</strong> No User ID or Password Required <strong>On-Campus Access:</strong> No User ID or Password Required |
| Ֆիզիկական նկարագրություն: | 1 online resource (37 pages) |
| Ձևաչափ: | Mode of access: Internet |
| ISSN: | 1018-5941 |
| Հասանելի: | Electronic access restricted to authorized BRAC University faculty, staff and students |