Advances in credit risk modelling and corporate bankruptcy prediction:
"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. Models and techniques are illustrated with empirical examples and are accompanied by a careful expla...
Saved in:
| Format: | Book |
|---|---|
| Language: | English |
| Published: |
Cambridge [u.a.]
Cambridge Univ. Press
2008
|
| Edition: | 1. publ. |
| Series: | Quantitative methods for applied economics and business research
|
| Subjects: | |
| Online Access: | Contributor biographical information Publisher description Table of contents only Inhaltsverzeichnis |
| Summary: | "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. 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."--BOOK JACKET. |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | X, 298 S. graph. Darst. |
| ISBN: | 9780521869287 9780521689540 |
Staff View
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Record in the Search Index
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| adam_text | Contents
List of figures page vii
List of tables viii
List of contributors x
Introduction 1
Stewart Jones and David A. Hensher
A statistical model for credit scoring 14
William H. Greene
Mixed logit and error component models of corporate insolvency
and bankruptcy risk 44
David A. Hensher and Stewart Jones
An evaluation of open- and closed-form distress prediction
models: The nested logit and latent class models 80
Stewart Jones and David A. Hensher
Survival analysis and omitted dividends 114
Marc J. Leclere
Non-parametric methods for credit risk analysis: Neural networks
and recursive partitioning techniques 137
Maurice Peat
Bankruptcy prediction and structural credit risk models 154
Andreas Charitou, Neophytos Lambertides and Lenos Trigeorgis
Default recovery rates and LGD in credit risk modelling and
practice: An updated review of the literature and empirical evidence 175
Edward I. Altman
Contents
8 Credit derivatives: Current practices and controversies 207
Stewart Jones and Maurice Peat
9 Local government distress in Australia: A latent class regression
analysis 242
Stewart Jones and Robert G. Walker
10 A belief-function perspective to credit risk assessments 269
Rajendra P. Srivastava and Stewart Jones
Index 295
Figures
1.1 Model predictions of profits vs. default probabilities page 37
1.2 Expected profit vs. expected expenditure 38
1.3 Normalized expected profits 40
1.4 Profits vs. default probability 40
2.1 Bivariate scatter plot of Halton (7) and Halton (9) 55
3.1 Latent effects on corporate failure 94
3.2 Nested tree structure for states of financial distress 102
4.1 Kaplan-Meier estimator of survival function of omitted
dividend payments 129
4.2 Kaplan-Meier estimator of survival function of omitted dividend
payments stratified by income/assets 130
5.1 Single hidden layer neural network structure 141
5.2 Classification tree with two splits and three terminal nodes 151
7.1 Dollar weighted average recovery rates to dollar weighted average
default rates (1982-2006) 186
8.1 A CDO structure 215
8.2 A synthetic CDO structure 216
8.3 Time series plot of daily Merton bankruptcy probabilities 237
8.4 Time series plot of daily reduced form bankruptcy probabilities 239
10.1 Evidential diagram for a rudimentary default risk model 284
10.2 Effect of reputation cost on desired level of default risk 291
Tables
1.1 Variables used in analysis of credit card default page 24
1.2 Descriptive statistics for variables 26
1.3 Sampling weights for choice-based sampling 27
1.4 Weighted and unweighted probit cardholder equations 29
1.5 Estimated expenditure equation 30
1.6 Average predicted expenditures 31
1.7 Default model 32
1.8 Estimated cardholder equation joint with default equation 33
1.9 Estimated default probabilities 35
1.10 Predictions for different thresholds 36
1.11 Sample average expected profits 37
1.12 Normalized expected profits 39
2.1 Definition of variables 63
2.2 Panel A: Fixed parameter estimates and f-values for final
multinomial error component logit and standard MNL models 65
2.2 Panel B: Random parameter and latent error component estimates
and t-values for final multinomial error component logit 66
2.2 Panel C: Log-likelihood at convergence and sample sizes for final
multinomial error component logit and standard MNL models 68
2.2 Panel D: Descriptive statistics for significant covariates reported in
panels A and B 68
2.2 Panel E: Direct elasticities for final multinomial error component
logit and standard MNL models 72
3.1 Summary of major strengths and challenges of different logit models 90
3.2 Model fit summary, parameter estimates (random and fixed) for
final nested logit, latent class MNL and mixed logit model 99
3.3 Forecasting performance of final multinomial nested logit, latent
class MNL and mixed logit models across distress states 0-3 107
4.1 IPO date and qualified audit opinion 119
4.2 Survival analysis models 131
List of tables
5.1 Neural network model fits 146
5.2 Classification tree results 151
5.3 Classification tree in rules form 152
6.1 Summary of main structural credit risk models 170
6.2 Structural models following Merton (1974) 171
6.3 Logistic regression for primary option variables 172
7.1 Recovery ratings from the ratings agencies 192
7.2 Recovery at default on public corporate bonds (1978-2006)
and bank loans (1989-2Q 2006) 198
7.3 Investment grade vs. non-investment grade (original rating) 198
7.4 Ultimate recovery rates on bank loan and bond defaults
(discounted values, 1988-2Q 2006) 199
7.5 The treatment of LGD and default rates within different credit
risk models 201
8.1 Global CDO market issuance data 219
8.2 Average 1 year default rates 1983-2000 (Moody s) 234
8.3 Recovery rates on corporate bonds from Moody s Investor s
Service (2000) 234
8.4 CDS premium calculation 235
8.5 CDS premium under structural probabilities 240
8.6 CDS premium under reduced form probabilities 240
9.1 Latent class regression analysis (1 class) for quantitative measures
(i.e. physical output levels) of service delivery 249
9.2 Model fit and prediction statistics for a two-class latent
regression model 257
9.3 Parameter estimates, wald statistics, Z values, means and
standard deviations for latent class model 258
9.4 Comparison of financial performance of urban vs. rural councils 262
|
| adam_txt |
Contents
List of figures page vii
List of tables viii
List of contributors x
Introduction 1
Stewart Jones and David A. Hensher
A statistical model for credit scoring 14
William H. Greene
Mixed logit and error component models of corporate insolvency
and bankruptcy risk 44
David A. Hensher and Stewart Jones
An evaluation of open- and closed-form distress prediction
models: The nested logit and latent class models 80
Stewart Jones and David A. Hensher
Survival analysis and omitted dividends 114
Marc J. Leclere
Non-parametric methods for credit risk analysis: Neural networks
and recursive partitioning techniques 137
Maurice Peat
Bankruptcy prediction and structural credit risk models 154
Andreas Charitou, Neophytos Lambertides and Lenos Trigeorgis
Default recovery rates and LGD in credit risk modelling and
practice: An updated review of the literature and empirical evidence 175
Edward I. Altman
Contents
8 Credit derivatives: Current practices and controversies 207
Stewart Jones and Maurice Peat
9 Local government distress in Australia: A latent class regression
analysis 242
Stewart Jones and Robert G. Walker
10 A belief-function perspective to credit risk assessments 269
Rajendra P. Srivastava and Stewart Jones
Index 295
Figures
1.1 Model predictions of profits vs. default probabilities page 37
1.2 Expected profit vs. expected expenditure 38
1.3 Normalized expected profits 40
1.4 Profits vs. default probability 40
2.1 Bivariate scatter plot of Halton (7) and Halton (9) 55
3.1 Latent effects on corporate failure 94
3.2 Nested tree structure for states of financial distress 102
4.1 Kaplan-Meier estimator of survival function of omitted
dividend payments 129
4.2 Kaplan-Meier estimator of survival function of omitted dividend
payments stratified by income/assets 130
5.1 Single hidden layer neural network structure 141
5.2 Classification tree with two splits and three terminal nodes 151
7.1 Dollar weighted average recovery rates to dollar weighted average
default rates (1982-2006) 186
8.1 A CDO structure 215
8.2 A synthetic CDO structure 216
8.3 Time series plot of daily Merton bankruptcy probabilities 237
8.4 Time series plot of daily reduced form bankruptcy probabilities 239
10.1 Evidential diagram for a rudimentary default risk model 284
10.2 Effect of reputation cost on desired level of default risk 291
Tables
1.1 Variables used in analysis of credit card default page 24
1.2 Descriptive statistics for variables 26
1.3 Sampling weights for choice-based sampling 27
1.4 Weighted and unweighted probit cardholder equations 29
1.5 Estimated expenditure equation 30
1.6 Average predicted expenditures 31
1.7 Default model 32
1.8 Estimated cardholder equation joint with default equation 33
1.9 Estimated default probabilities 35
1.10 Predictions for different thresholds 36
1.11 Sample average expected profits 37
1.12 Normalized expected profits 39
2.1 Definition of variables 63
2.2 Panel A: Fixed parameter estimates and f-values for final
multinomial error component logit and standard MNL models 65
2.2 Panel B: Random parameter and latent error component estimates
and t-values for final multinomial error component logit 66
2.2 Panel C: Log-likelihood at convergence and sample sizes for final
multinomial error component logit and standard MNL models 68
2.2 Panel D: Descriptive statistics for significant covariates reported in
panels A and B 68
2.2 Panel E: Direct elasticities for final multinomial error component
logit and standard MNL models 72
3.1 Summary of major strengths and challenges of different logit models 90
3.2 Model fit summary, parameter estimates (random and fixed) for
final nested logit, latent class MNL and mixed logit model 99
3.3 Forecasting performance of final multinomial nested logit, latent
class MNL and mixed logit models across distress states 0-3 107
4.1 IPO date and qualified audit opinion 119
4.2 Survival analysis models 131
List of tables
5.1 Neural network model fits 146
5.2 Classification tree results 151
5.3 Classification tree in rules form 152
6.1 Summary of main structural credit risk models 170
6.2 Structural models following Merton (1974) 171
6.3 Logistic regression for primary option variables 172
7.1 Recovery ratings from the ratings agencies 192
7.2 Recovery at default on public corporate bonds (1978-2006)
and bank loans (1989-2Q 2006) 198
7.3 Investment grade vs. non-investment grade (original rating) 198
7.4 Ultimate recovery rates on bank loan and bond defaults
(discounted values, 1988-2Q 2006) 199
7.5 The treatment of LGD and default rates within different credit
risk models 201
8.1 Global CDO market issuance data 219
8.2 Average 1 year default rates 1983-2000 (Moody's) 234
8.3 Recovery rates on corporate bonds from Moody's Investor's
Service (2000) 234
8.4 CDS premium calculation 235
8.5 CDS premium under structural probabilities 240
8.6 CDS premium under reduced form probabilities 240
9.1 Latent class regression analysis (1 class) for quantitative measures
(i.e. physical output levels) of service delivery 249
9.2 Model fit and prediction statistics for a two-class latent
regression model 257
9.3 Parameter estimates, wald statistics, Z values, means and
standard deviations for latent class model 258
9.4 Comparison of financial performance of urban vs. rural councils 262 |
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| illustrated | Illustrated |
| index_date | 2024-07-02T22:05:13Z |
| indexdate | 2024-07-09T21:21:37Z |
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| isbn | 9780521869287 9780521689540 |
| language | English |
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| record_format | marc |
| series2 | Quantitative methods for applied economics and business research |
| spelling | Advances in credit risk modelling and corporate bankruptcy prediction ed. by Stewart Jones and David A. Hensher 1. publ. Cambridge [u.a.] Cambridge Univ. Press 2008 X, 298 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Quantitative methods for applied economics and business research Includes bibliographical references and index "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. 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."--BOOK JACKET. Credit Management Risk management Bankruptcy Forecasting Insolvenz (DE-588)4072843-2 gnd rswk-swf Kreditrisiko (DE-588)4114309-7 gnd rswk-swf Prognose (DE-588)4047390-9 gnd rswk-swf Mathematisches Modell (DE-588)4114528-8 gnd rswk-swf Kreditrisiko (DE-588)4114309-7 s Prognose (DE-588)4047390-9 s Mathematisches Modell (DE-588)4114528-8 s DE-604 Insolvenz (DE-588)4072843-2 s Jones, Stewart 1964- Sonstige (DE-588)139244581 oth Hensher, David A. 1947- Sonstige (DE-588)142773298 oth http://www.loc.gov/catdir/enhancements/fy0834/2008017482-b.html Contributor biographical information http://www.loc.gov/catdir/enhancements/fy0834/2008017482-d.html Publisher description http://www.loc.gov/catdir/enhancements/fy0834/2008017482-t.html Table of contents only HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016743329&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Advances in credit risk modelling and corporate bankruptcy prediction Credit Management Risk management Bankruptcy Forecasting Insolvenz (DE-588)4072843-2 gnd Kreditrisiko (DE-588)4114309-7 gnd Prognose (DE-588)4047390-9 gnd Mathematisches Modell (DE-588)4114528-8 gnd |
| subject_GND | (DE-588)4072843-2 (DE-588)4114309-7 (DE-588)4047390-9 (DE-588)4114528-8 |
| title | Advances in credit risk modelling and corporate bankruptcy prediction |
| title_auth | Advances in credit risk modelling and corporate bankruptcy prediction |
| title_exact_search | Advances in credit risk modelling and corporate bankruptcy prediction |
| title_exact_search_txtP | Advances in credit risk modelling and corporate bankruptcy prediction |
| title_full | Advances in credit risk modelling and corporate bankruptcy prediction ed. by Stewart Jones and David A. Hensher |
| title_fullStr | Advances in credit risk modelling and corporate bankruptcy prediction ed. by Stewart Jones and David A. Hensher |
| title_full_unstemmed | Advances in credit risk modelling and corporate bankruptcy prediction ed. by Stewart Jones and David A. Hensher |
| title_short | Advances in credit risk modelling and corporate bankruptcy prediction |
| title_sort | advances in credit risk modelling and corporate bankruptcy prediction |
| topic | Credit Management Risk management Bankruptcy Forecasting Insolvenz (DE-588)4072843-2 gnd Kreditrisiko (DE-588)4114309-7 gnd Prognose (DE-588)4047390-9 gnd Mathematisches Modell (DE-588)4114528-8 gnd |
| topic_facet | Credit Management Risk management Bankruptcy Forecasting Insolvenz Kreditrisiko Prognose Mathematisches Modell |
| url | http://www.loc.gov/catdir/enhancements/fy0834/2008017482-b.html http://www.loc.gov/catdir/enhancements/fy0834/2008017482-d.html http://www.loc.gov/catdir/enhancements/fy0834/2008017482-t.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016743329&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT jonesstewart advancesincreditriskmodellingandcorporatebankruptcyprediction AT hensherdavida advancesincreditriskmodellingandcorporatebankruptcyprediction |