Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction

doi:10.1017/CBO9780511754197

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.

‘… 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.’

Source: The Times Higher Education Supplement

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Contents

Frontmatter
pp i-iv
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Contents
pp v-vi
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List of figures
pp vii-vii
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List of tables
pp viii-ix
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List of contributors
pp x-x
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Introduction
pp 1-13
Introduction
- By Stewart Jones, David A. Hensher
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1 - A statistical model for credit scoring
pp 14-43
- By William H. Greene
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2 - Mixed logit and error component models of corporate insolvency and bankruptcy risk
pp 44-79
- By David A. Hensher, Stewart Jones
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3 - An evaluation of open- and closed-form distress prediction models: The nested logit and latent class models
pp 80-113
- By Stewart Jones, David A. Hensher
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4 - Survival analysis and omitted dividends
pp 114-136
- By Marc J. Leclere
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5 - Non-parametric methods for credit risk analysis: Neural networks and recursive partitioning techniques
pp 137-153
- By Maurice Peat
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6 - Bankruptcy prediction and structural credit risk models
pp 154-174
- By Andreas Charitou, Neophytos Lambertides, Lenos Trigeorgis
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7 - Default recovery rates and LGD in credit risk modelling and practice: An updated review of the literature and empirical evidence
pp 175-206
An updated review of the literature and empirical evidence*
- By Edward I. Altman
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8 - Credit derivatives: Current practices and controversies
pp 207-241
- By Stewart Jones, Maurice Peat
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9 - Local government distress in Australia: A latent class regression analysis
pp 242-268
- By Stewart Jones, Robert G. Walker
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10 - A belief-function perspective to credit risk assessments
pp 269-294
- By Rajendra P. Srivastava, Stewart Jones
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Index
pp 295-298
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Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction

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Contents

Frontmatter
pp i-iv

Contents
pp v-vi

List of figures
pp vii-vii

List of tables
pp viii-ix

List of contributors
pp x-x

Introduction
pp 1-13

1 - A statistical model for credit scoring
pp 14-43

2 - Mixed logit and error component models of corporate insolvency and bankruptcy risk
pp 44-79

3 - An evaluation of open- and closed-form distress prediction models: The nested logit and latent class models
pp 80-113

4 - Survival analysis and omitted dividends
pp 114-136

5 - Non-parametric methods for credit risk analysis: Neural networks and recursive partitioning techniques
pp 137-153

6 - Bankruptcy prediction and structural credit risk models
pp 154-174

7 - Default recovery rates and LGD in credit risk modelling and practice: An updated review of the literature and empirical evidence
pp 175-206

8 - Credit derivatives: Current practices and controversies
pp 207-241

9 - Local government distress in Australia: A latent class regression analysis
pp 242-268

10 - A belief-function perspective to credit risk assessments
pp 269-294

Index
pp 295-298

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Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction

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