TY - CHAP
T1 - Empirical studies of structural credit risk models and the application in default prediction
T2 - Review and new evidence
AU - Lee, Han Hsing
AU - Chen, Ren Raw
AU - Lee, Cheng Few
N1 - Publisher Copyright:
© 2021 by World Scientific Publishing Co. Pte. Ltd.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - 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.
AB - 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.
KW - Default prediction
KW - Down-and-out barrier model
KW - Estimation approach
KW - KMV estimation method
KW - Maximum likelihood estimation (MLE)
KW - Monte carlo experiment
KW - Structural credit risk model
UR - http://www.scopus.com/inward/record.url?scp=85096280926&partnerID=8YFLogxK
U2 - 10.1142/9789811202391_0050
DO - 10.1142/9789811202391_0050
M3 - 章节
AN - SCOPUS:85096280926
SN - 9789811202384
SP - 1845
EP - 1901
BT - Handbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning (In 4 Volumes)
PB - World Scientific Publishing Co.
ER -