Empirical studies of structural credit risk models and the application in default prediction: Review and new evidence

Han-Hsing Lee*, Ren Raw Chen, Cheng Few Lee

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

This paper first reviews empirical evidence and estimation methods of structural credit risk models. Next, an empirical investigation of the performance of default prediction under the down-and-out barrier option framework is provided. In the literature review, a brief overview of the structural credit risk models is provided. Empirical investigations in extant literature papers are described in some detail, and their results are summarized in terms of subject and estimation method adopted in each paper. Current estimation methods and their drawbacks are discussed in detail. In our empirical investigation, we adopt the Maximum Likelihood Estimation method proposed by Duan [Mathematical Finance 10 (1994) 461462]. This method has been shown by Ericsson and Reneby [Journal of Business 78 (2005) 707735] through simulation experiments to be superior to the volatility restriction approach commonly adopted in the literature. Our empirical results surprisingly show that the simple Merton model outperforms the Brockman and Turtle [Journal of Financial Economics 67 (2003) 511529] model in default prediction. The inferior performance of the Brockman and Turtle model may be the result of its unreasonable assumption of the flat barrier.

Original languageEnglish
Pages (from-to)629-675
Number of pages47
JournalInternational Journal of Information Technology and Decision Making
Volume8
Issue number4
DOIs
StatePublished - 1 Dec 2009

Keywords

  • Default prediction
  • Estimation approach
  • Maximum Likelihood Estimation (MLE)
  • Structural credit risk model

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