Name
Abstract | ||
|---|---|---|
•New support vector regression (SVR) techniques are applied to recovery rates of corporate bonds.•The proposed techniques outperform other methods significantly.•We modify the SVR algorithm to account for heterogeneity of bond seniorities.•Transformation of recovery rates does not improve the prediction accuracy.•Improved SVR models capture the features of each segment better than segmented ones. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.ejor.2014.06.043 | European Journal of Operational Research |
Keywords | Field | DocType |
Support vector regression,Loss given default,Recovery rate,Credit risk modelling | Least squares,Econometrics,Basel Accords,Bond,Economic capital,Support vector machine,Loss given default,Statistical model,Mathematics,Computation | Journal |
Volume | Issue | ISSN |
240 | 2 | 0377-2217 |
Citations | PageRank | References |
15 | 0.73 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yao Xiao | 1 | 64 | 9.74 |
| Jonathan Crook | 2 | 197 | 14.31 |
| Galina Andreeva | 3 | 61 | 6.13 |