A hybrid information approach to predict corporate credit risk
Yin Liao and
Journal of Futures Markets, 2018, vol. 38, issue 9, 1062-1078
This study proposes a hybrid information approach to predict corporate credit risk. In contrast to the previous literature that debates which credit risk model is the best, we pool information from a diverse set of structural and reduced‐form models to produce a model combination based on credit risk prediction. Compared with each single model, the pooled strategies yield consistently lower average risk prediction errors over time. We also find that while the reduced‐form models contribute more in the pooled strategies for speculative‐grade names and longer maturities, the structural models have higher weights for shorter maturities and investment grade names.
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:wly:jfutmk:v:38:y:2018:i:9:p:1062-1078
Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0270-7314
Access Statistics for this article
Journal of Futures Markets is currently edited by Robert I. Webb
More articles in Journal of Futures Markets from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().