logo

EbookBell.com

Most ebook files are in PDF format, so you can easily read them using various software such as Foxit Reader or directly on the Google Chrome browser.
Some ebook files are released by publishers in other formats such as .awz, .mobi, .epub, .fb2, etc. You may need to install specific software to read these formats on mobile/PC, such as Calibre.

Please read the tutorial at this link:  https://ebookbell.com/faq 


We offer FREE conversion to the popular formats you request; however, this may take some time. Therefore, right after payment, please email us, and we will try to provide the service as quickly as possible.


For some exceptional file formats or broken links (if any), please refrain from opening any disputes. Instead, email us first, and we will try to assist within a maximum of 6 hours.

EbookBell Team

Advances In Credit Risk Modelling And Corporate Bankruptcy Prediction Jones S

  • SKU: BELL-2598748
Advances In Credit Risk Modelling And Corporate Bankruptcy Prediction Jones S
$ 31.00 $ 45.00 (-31%)

4.1

80 reviews

Advances In Credit Risk Modelling And Corporate Bankruptcy Prediction Jones S instant download after payment.

Publisher: CUP
File Extension: PDF
File size: 1.12 MB
Pages: 310
Author: Jones S., Hensher D.A. (eds.)
ISBN: 9780521869287, 0521869285
Language: English
Year: 2008

Product desciption

Advances In Credit Risk Modelling And Corporate Bankruptcy Prediction Jones S by Jones S., Hensher D.a. (eds.) 9780521869287, 0521869285 instant download after payment.

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.

Related Products