Skip to main navigation Skip to search Skip to main content

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

  • National Yang Ming Chiao Tung University
  • Fordham University
  • Rutgers - The State University of New Jersey, New Brunswick

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

This chapter 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 (1994). This method has been shown by Ericsson and Reneby (2005) 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 (2003) 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
Title of host publicationHandbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning (In 4 Volumes)
PublisherWorld Scientific Publishing Co.
Pages1845-1901
Number of pages57
ISBN (Electronic)9789811202391
ISBN (Print)9789811202384
DOIs
StatePublished - 01 01 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 by World Scientific Publishing Co. Pte. Ltd.

Keywords

  • Default prediction
  • Down-and-out barrier model
  • Estimation approach
  • KMV estimation method
  • Maximum likelihood estimation (MLE)
  • Monte carlo experiment
  • Structural credit risk model

Fingerprint

Dive into the research topics of 'Empirical studies of structural credit risk models and the application in default prediction: Review and new evidence'. Together they form a unique fingerprint.

Cite this