EconPapers    
Economics at your fingertips  
 

Improving corporate bond recovery rate prediction using multi-factor support vector regressions

Abdolreza Nazemi, Konstantin Heidenreich and Frank Fabozzi ()

European Journal of Operational Research, 2018, vol. 271, issue 2, 664-675

Abstract: In the multi-factor framework described in this paper, we use instrument-specific characteristics, several macroeconomic variables, and industry-specific characteristics as our explanatory variables for predicting recovery rates for corporate bonds. By including the principal components derived from a large number of macroeconomic variables, all three least-squares support vector regression methods, as well as the ordinary linear regression, exhibit higher out-of-sample predictive accuracy than the models that included only the few macroeconomic variables suggested in the literature. We compare the prediction accuracies of all techniques by incorporating sparse principal components, nonlinear principal components from an auto-associative neural network, and kernel principal components. Our results show that sparse principal components generate more interpretable and accurate estimations compared to the other principal component techniques. Moreover, we apply gradient boosting to generate a ranking of the 104 macroeconomic variables, from best to worst, based on their prediction power in recovery rate estimation. The three categories with the most informative macroeconomic predictors are micro-level factors, business cycle variables, and stock market indicators.

Keywords: Recovery rate; Least-squares support vector regression methods; Sparse principal component analysis; Kernel principal component analysis; Gradient boosting (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2) Track citations by RSS feed

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0377221718304247
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:271:y:2018:i:2:p:664-675

Access Statistics for this article

European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().

 
Page updated 2019-10-05
Handle: RePEc:eee:ejores:v:271:y:2018:i:2:p:664-675