A hybrid information approach to predict corporate credit risk

Di Bu, Simone Kelly, Yin Liao, Qing Zhou

Research output: Contribution to journalArticleResearchpeer-review

Abstract

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.
LanguageEnglish
Pages1062-1078
Number of pages17
JournalThe Journal of Futures Markets
Volume38
Issue number9
Early online date21 May 2018
DOIs
Publication statusPublished - Sep 2018

Fingerprint

Reduced-form model
Credit risk
Maturity
Prediction error
Prediction
Credit risk models
Structural model

Keywords

  • bond spread
  • corporate credit risk
  • model combination
  • reduced‐form model
  • structural model

Cite this

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A hybrid information approach to predict corporate credit risk. / Bu, Di; Kelly, Simone; Liao, Yin; Zhou, Qing.

In: The Journal of Futures Markets, Vol. 38, No. 9, 09.2018, p. 1062-1078.

Research output: Contribution to journalArticleResearchpeer-review

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