E-Book Overview
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.
E-Book Content
This page intentionally left blank 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. Stewart Jones is Professor of Accounting at the University of Sydney. He has published extensively in the area of credit risk and corporate bankruptcy, and is co-editor of the leading international accounting and finance journal, Abacus. David A. Hensher is Professor of Management at the University of Sydney. He is the author of numerous books and articles on discrete choice models, including Stated Choice Methods (Cambridge University Press, 2000) and Applied Choice Analysis (Cambridge University Press, 2005). He teaches discrete choice modelling to academic, business and government audiences, and is also a partner in Econometric Software, the developers of Nlogit and Limdep. Quantitative Methods for Applied Economics and Business Research Series Editor PROFESSOR PHILIP HANS FRANSES Erasmus University, Rotterdam Researchers and practitioners in applied economics and business now have access to a much richer and more varied choice of data than earlier generations. Quantitative Methods for Applied Economics and Business Research is a new series aimed at meeting the needs of graduate students, researchers and practitioners who have a basic grounding in statistical analysis and who wish to take advantage of more sophisticated methodology in their work. Published titles Lusk and Shogren (eds.) Experimental Auctions Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction Edited by Stewart Jones and David A. Hensher CAMBRIDG