Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction
Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction
The field of credit risk and corporate bankruptcy prediction has gained considerable momentum following the collapse of many large corporations around the world, and more recently through the sub-prime scandal in the United States. This book provides a thorough compendium of the different modelling approaches available in the field, including several new techniques that extend the horizons of future research and practice. Topics covered include probit models (in particular bivariate probit modelling), advanced logistic regression models (in particular mixed logit, nested logit and latent class models), survival analysis models, non-parametric techniques (particularly neural networks and recursive partitioning models), structural models and reduced form (intensity) modelling. Models and techniques are illustrated with empirical examples and are accompanied by a careful explanation of model derivation issues. This practical and empirically-based approach makes the book an ideal resource for all those concerned with credit risk and corporate bankruptcy, including academics, practitioners and regulators.
- Presents a wide range of modelling approaches, giving readers an insight into both traditional and new techniques
- Covers all the latest developments in the field, including the recent sub-prime collapse
- Empirical illustrations used to aid comprehension
Reviews & endorsements
'… if you wish to learn more about the nature of the financial instruments that have brought the world to its knees, then this … is a useful starting point.' The Times Higher Education Supplement
Product details
October 2008Adobe eBook Reader
9780511426827
0 pages
0kg
18 b/w illus. 39 tables
This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table of Contents
- List of figures
- List of tables
- List of contributors
- Introduction Stewart Jones and David A. Hensher
- 1. A statistical model for credit scoring William H. Greene
- 2. Mixed Logit and error component models of corporate insolvency and bankruptcy risk Stewart Jones and David A. Hensher
- 3. An evaluation of open and closed form distress prediction models: the nested Logit and latent class models Stewart Jones and David A. Hensher
- 4. Survival analysis and omitted dividends Marc J. Leclere
- 5. Non-parametric methods for credit risk analysis: neural networks and recursive partitioning techniques Maurice Peat
- 6. Bankruptcy prediction and structural credit risk models Andreas Charitou, Neophytos Lambertides and Lenos Trigeorgis
- 7. Default recovery rates and LGD in credit risk modeling and practice: an updated review of the literature and empirical evidence Edward I. Altman
- 8. Credit derivatives: current practices and controversies Stewart Jones and Maurice Peat
- 9. Local government distress in Australia: a latent class regression analysis Stewart Jones and Robert G. Walker
- 10. A belief-function perspective to credit risk assessments Rajendra P. Srivastava and Stewart Jones
- Index.
