A hybrid information approach to predict corporate credit risk

Di Bu, Simone Kelly, Yin Liao*, Qing Zhou

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

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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.

Original languageEnglish
Pages (from-to)1062-1078
Number of pages17
JournalJournal of Futures Markets
Issue number9
Early online date21 May 2018
Publication statusPublished - Sep 2018


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