| Description |
1 online resource (37 pages) |
| Series |
IMF Working Paper ; WP/19/292 |
|
IMF working paper ; WP/19/292.
|
| 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 |
| Notes |
Print version record |
| Subject |
Asset Pricing.
|
|
Forecasting and Other Model Applications.
|
|
General Financial Markets.
|
|
General.
|
|
Model Evaluation and Testing.
|
| Form |
Electronic book
|
| Author |
Li, Jian, author
|
|
Meyer Cirkel, Alexis, author.
|
|
International Monetary Fund, issuing body.
|
| ISBN |
1513524089 |
|
9781513524085 |
|
