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Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction (Quantitative Methods for Applied Economics and Business Research)

Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction (Quantitative Methods for Applied Economics and Business Research)

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Stewart Jones
Cambridge University Press, 9/25/2008
EAN 9780521689540, ISBN10: 0521689546

Paperback, 308 pages, 24.7 x 17.4 x 1.8 cm
Language: English

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.

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.

'... 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.' Times Higher Education