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

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9780521689540: Advances in Credit Risk Modelling and Corporate Bankruptcy Prediction (Quantitative Methods for Applied Economics and Business Research)
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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.

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Book Description:

A thorough compendium of credit risk modelling approaches, including several new techniques that extend the horizons of future research and practice. Models and techniques are illustrated with empirical examples and are accompanied by a careful explanation of model derivation issues. An ideal resource for academics, practitioners and regulators.

About the Author:

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.

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Book Description CAMBRIDGE UNIVERSITY PRESS, United Kingdom, 2008. Paperback. Condition: New. Language: English. Brand new Book. 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. Seller Inventory # AAA9780521689540

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Book Description CAMBRIDGE UNIVERSITY PRESS, United Kingdom, 2008. Paperback. Condition: New. Language: English. Brand new Book. 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. Seller Inventory # AAA9780521689540

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