On the dynamics of credit risk: an econometric analysis
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin
dissertation.de
2007
|
| Ausgabe: | Als Ms. gedr. |
| Schlagworte: | |
| Online-Zugang: | Ausführliche Beschreibung Inhaltsverzeichnis |
| Beschreibung: | Zugl.: Ulm, Univ., Diss., 2007 |
| Beschreibung: | X, 201 S. graph. Darst. 210 mm x 150 mm, 293 gr. |
| ISBN: | 9783866242470 3866242476 |
Internformat
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| 264 | 1 | |a Berlin |b dissertation.de |c 2007 | |
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Datensatz im Suchindex
| _version_ | 1804136935636074496 |
|---|---|
| adam_text | Ill
Contents
1 Introduction 1
2 Ways to measure a company s credit risk 9
2.1 Credit ratings............................... 11
2.1.1 The rating industry and the rating process........... 11
2.1.2 Rating methodology and rating policy ............. 20
2.1.3 Measurement and examination of rating migrations...... 24
2.1.4 Accuracy of ratings in assessing the default risk........ 44
2.1.5 Summary of rating policy and stylized facts .......... 47
2.2 Structural models............................. 50
2.2.1 Modelling a firm s default .................... 50
2.2.2 Extending the basic Merton model............... 54
2.2.3 How to estimate structural models?............... 58
2.2.4 The expected default frequency................. 61
2.3 Scoring models.............................. 67
2.3.1 Ordered response models..................... 71
2.3.2 Goodness-of-fit and specification tests.............. 73
2.3.3 Population correction and rare events.............. 76
2.3.4 Generalizing the transformation function............ 77
2.3.5 Testing non-nested alternatives................. 80
2.3.6 Examining the effects of transformation functions....... 83
2.4 Static analysis of risk measures ..................... 89
2.4.1 Comparing the Performance in Default Prediction....... 91
2.4.2 Explaining the rating by financial ratios ............ 94
IV CONTENTS
3 Dynamics of ratings and market-based risk measures 107
3.1 An econometric model of rating stickiness ...............110
3.2 Variable definition ............................116
3.3 Stickiness of rating actions........................120
3.3.1 Basic model............................121
3.3.2 Sensitivity of thresholds.....................124
3.3.3 Quantification of the methodology bias.............132
3.3.4 Quantifying factors of stickiness.................135
3.3.5 Outlooks and watchlists.....................145
4 What do rating agencies know when? 149
4.1 Dynamic panel model with ratings as explanatory variable...... 150
4.2 Selection bias............................... 155
4.3 Univariate results of the dynamic panel model............. 159
4.3.1 A horse race of Moody s and S P................ 163
4.3.2 Predictability of the EDF .................... 164
4.3.3 Differences of investment and subinvestment grade....... 166
4.4 Multivariate results using a scoring model ............... 169
5 Conclusion 173
List of Figures
2.1 Illustration of through-the-cycle vs. point-in-time rating system ... 21
2.2 Number of issuers of the Moody s database over time.........25
2.3 Illustration of rating momentum and reversal..............36
2.4 Reversal intensity over time.......................39
2.5 Default rates per year...........................44
2.6 Construction of the accuracy ratio with a cumulative accuracy profile 45
2.7 Cumulative accuracy profile of Moody s ratings ............46
2.8 Payoff structure of the Merton (1974) model..............53
2.9 Illustration of Merlon s structural model................54
2.10 Illustration of a first-passage-default model...............56
2.11 Mean and median of the EDF over time (in %) ............64
2.12 Illustration of the scobit function.................... 79
2.13 Gumbel distribution, an asymmetric transformation function.....81
2.14 Comparing the accuracy ratio for logit and Burr models over time . . 87
2.15 Comparing S P and Moody s rated issuers over time.........90
2.16 Proportion of debt issued by rated companies to total issued debt . . 90
3.1 Illustration of the friction models....................112
3.2 Graphical representation of the threshold conversion procedure . . . .115
3.3 Thresholds per rating grade.......................125
3.4 Thresholds per letter grade rating ...................126
3.5 Describing the thresholds per rating class by a polynomial......127
3.6 Change of thresholds over the years...................130
3.7 How thresholds depend on the horizon.................131
3.8 Illustration of the EDF trend ......................133
VI LIST OF FIGURES
3.9 Example of the simulated rating for an arbitrary issuer........134
3.10 Example of ratings predicted bj the friction model ..........142
3.11 Effect of the initial rating ........................145
4.1 Dynamic panel model using the EDF..................166
4.2 Dynamic model using a scoring model s prediction...........170
4.3 Scoring model s prediction including market leverage .........171
VII
List of Tables
2.1 Types of credit risk and their measurements.............. 10
2.2 Moody s and S P rating system with numerical conversion...... 15
2.3 The broader letter grade rating system................. 16
2.4 Descriptive statistics of the Moody s database............. 26
2.6 Generator and one year transition matrix for letter grade ratings ... 30
2.5 One year transition matrix (in %) based on the hazard rate approach 31
2.7 95% confidence sets for transitions to the default category ...... 34
2.8 Unconditional cumulative upgrade and downgrade probability .... 35
2.9 Reversal intensity over time....................... 41
2.10 Theoretical reversal and momentum probabilities over time...... 42
2.11 Payouts for call option, barrier option and bond values........ 57
2.12 Descriptive statistics of the EDF per rating class............ 65
2.13 Centiles of the EDF per rating class................... 65
2.14 Mean and variance for different parameters in the Burr model .... 80
2.15 Descriptive Statistics for the scoring model s variables......... 84
2.16 Marginal Effects of the scoring model s variables............ 85
2.17 Comparing logit, probit and Burr models................ 86
2.18 Difference in the accuracy ratio for logit and Burr models....... 88
2.19 Ratings and market-value in default-prediction............. 93
2.20 Debt over total capitalization by rating class (in %).......... 95
2.21 Three-year medians of financial ratios.................. 97
2.22 Descriptive statistics of Moody s ratings for S P key ratios...... 98
2.23 Descriptive statistics of S P ratings for S P key ratios........ 99
2.24 Pooled regression of ratings on S P key ratios............. 100
2.25 Pooled regression of ratings on Metz (2006) variables......... 102
VIII LIST OF TABLES
2.26 Pooled Regression of ratings on Blume et al. (1998) variables.....104
2.27 Explaining ratings in a pooled model..................106
3.1 Idealized probability of default......................118
3.2 Robustness checks for choice of non-overlapping time period.....121
3.3 Initial model of one-year rating changes.................122
3.4 Descriptive statistics for the EDF and its trend ............134
3.5 Impact of the through-the-cycle and bucketing methodology.....135
3.6 Factors influencing the stickiness of rating actions...........137
3.7 Descriptive statistics for issuers with different initial rating......144
3.8 Friction model using outlook and watchlist adjusted ratings......146
3.9 Impact of outlook and watchlists on rating thresholds.........147
4.1 Univariate results of the dynamic panel model.............160
4.2 An univariate horse race of Moody s and S P.............165
4.3 Univariate predictive ability across the rating universe.........168
4.4 Multivariate predictive ability across the rating universe .......170
|
| adam_txt |
Ill
Contents
1 Introduction 1
2 Ways to measure a company's credit risk 9
2.1 Credit ratings. 11
2.1.1 The rating industry and the rating process. 11
2.1.2 Rating methodology and rating policy . 20
2.1.3 Measurement and examination of rating migrations. 24
2.1.4 Accuracy of ratings in assessing the default risk. 44
2.1.5 Summary of rating policy and stylized facts . 47
2.2 Structural models. 50
2.2.1 Modelling a firm's default . 50
2.2.2 Extending the basic Merton model. 54
2.2.3 How to estimate structural models?. 58
2.2.4 The expected default frequency. 61
2.3 Scoring models. 67
2.3.1 Ordered response models. 71
2.3.2 Goodness-of-fit and specification tests. 73
2.3.3 Population correction and rare events. 76
2.3.4 Generalizing the transformation function. 77
2.3.5 Testing non-nested alternatives. 80
2.3.6 Examining the effects of transformation functions. 83
2.4 Static analysis of risk measures . 89
2.4.1 Comparing the Performance in Default Prediction. 91
2.4.2 Explaining the rating by financial ratios . 94
IV CONTENTS
3 Dynamics of ratings and market-based risk measures 107
3.1 An econometric model of rating stickiness .110
3.2 Variable definition .116
3.3 Stickiness of rating actions.120
3.3.1 Basic model.121
3.3.2 Sensitivity of thresholds.124
3.3.3 Quantification of the methodology bias.132
3.3.4 Quantifying factors of stickiness.135
3.3.5 Outlooks and watchlists.145
4 What do rating agencies know when? 149
4.1 Dynamic panel model with ratings as explanatory variable. 150
4.2 Selection bias. 155
4.3 Univariate results of the dynamic panel model. 159
4.3.1 A horse race of Moody's and S P. 163
4.3.2 Predictability of the EDF . 164
4.3.3 Differences of investment and subinvestment grade. 166
4.4 Multivariate results using a scoring model . 169
5 Conclusion 173
List of Figures
2.1 Illustration of through-the-cycle vs. point-in-time rating system . 21
2.2 Number of issuers of the Moody's database over time.25
2.3 Illustration of rating momentum and reversal.36
2.4 Reversal intensity over time.39
2.5 Default rates per year.44
2.6 Construction of the accuracy ratio with a cumulative accuracy profile 45
2.7 Cumulative accuracy profile of Moody's ratings .46
2.8 Payoff structure of the Merton (1974) model.53
2.9 Illustration of Merlon's structural model.54
2.10 Illustration of a first-passage-default model.56
2.11 Mean and median of the EDF over time (in %) .64
2.12 Illustration of the scobit function. 79
2.13 Gumbel distribution, an asymmetric transformation function.81
2.14 Comparing the accuracy ratio for logit and Burr models over time . . 87
2.15 Comparing S P and Moody's rated issuers over time.90
2.16 Proportion of debt issued by rated companies to total issued debt . . 90
3.1 Illustration of the friction models.112
3.2 Graphical representation of the threshold conversion procedure . . . .115
3.3 Thresholds per rating grade.125
3.4 Thresholds per letter grade rating .126
3.5 Describing the thresholds per rating class by a polynomial.127
3.6 Change of thresholds over the years.130
3.7 How thresholds depend on the horizon.131
3.8 Illustration of the EDF trend .133
VI LIST OF FIGURES
3.9 Example of the simulated rating for an arbitrary issuer.134
3.10 Example of ratings predicted bj' the friction model .142
3.11 Effect of the initial rating .145
4.1 Dynamic panel model using the EDF.166
4.2 Dynamic model using a scoring model's prediction.170
4.3 Scoring model's prediction including market leverage .171
VII
List of Tables
2.1 Types of credit risk and their measurements. 10
2.2 Moody's and S P rating system with numerical conversion. 15
2.3 The broader letter grade rating system. 16
2.4 Descriptive statistics of the Moody's database. 26
2.6 Generator and one year transition matrix for letter grade ratings . 30
2.5 One year transition matrix (in %) based on the hazard rate approach 31
2.7 95% confidence sets for transitions to the default category . 34
2.8 Unconditional cumulative upgrade and downgrade probability . 35
2.9 Reversal intensity over time. 41
2.10 Theoretical reversal and momentum probabilities over time. 42
2.11 Payouts for call option, barrier option and bond values. 57
2.12 Descriptive statistics of the EDF per rating class. 65
2.13 Centiles of the EDF per rating class. 65
2.14 Mean and variance for different parameters in the Burr model . 80
2.15 Descriptive Statistics for the scoring model's variables. 84
2.16 Marginal Effects of the scoring model's variables. 85
2.17 Comparing logit, probit and Burr models. 86
2.18 Difference in the accuracy ratio for logit and Burr models. 88
2.19 Ratings and market-value in default-prediction. 93
2.20 Debt over total capitalization by rating class (in %). 95
2.21 Three-year medians of financial ratios. 97
2.22 Descriptive statistics of Moody's ratings for S P key ratios. 98
2.23 Descriptive statistics of S P ratings for S P key ratios. 99
2.24 Pooled regression of ratings on S P key ratios. 100
2.25 Pooled regression of ratings on Metz (2006) variables. 102
VIII LIST OF TABLES
2.26 Pooled Regression of ratings on Blume et al. (1998) variables.104
2.27 Explaining ratings in a pooled model.106
3.1 Idealized probability of default.118
3.2 Robustness checks for choice of non-overlapping time period.121
3.3 Initial model of one-year rating changes.122
3.4 Descriptive statistics for the EDF and its trend .134
3.5 Impact of the through-the-cycle and bucketing methodology.135
3.6 Factors influencing the stickiness of rating actions.137
3.7 Descriptive statistics for issuers with different initial rating.144
3.8 Friction model using outlook and watchlist adjusted ratings.146
3.9 Impact of outlook and watchlists on rating thresholds.147
4.1 Univariate results of the dynamic panel model.160
4.2 An univariate horse race of Moody's and S P.165
4.3 Univariate predictive ability across the rating universe.168
4.4 Multivariate predictive ability across the rating universe .170 |
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| dewey-ones | 332 - Financial economics |
| dewey-raw | 332.1 |
| dewey-search | 332.1 |
| dewey-sort | 3332.1 |
| dewey-tens | 330 - Economics |
| discipline | Wirtschaftswissenschaften |
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| index_date | 2024-07-02T18:27:23Z |
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| publisher | dissertation.de |
| record_format | marc |
| spelling | Posch, Peter N. Verfasser aut On the dynamics of credit risk an econometric analysis Peter N. Posch Als Ms. gedr. Berlin dissertation.de 2007 X, 201 S. graph. Darst. 210 mm x 150 mm, 293 gr. txt rdacontent n rdamedia nc rdacarrier Zugl.: Ulm, Univ., Diss., 2007 Ökonometrisches Modell (DE-588)4043212-9 gnd rswk-swf Kreditwesen (DE-588)4032950-1 gnd rswk-swf Rating (DE-588)4255219-9 gnd rswk-swf Kredit (DE-588)4032923-9 gnd rswk-swf Messung (DE-588)4038852-9 gnd rswk-swf Ausfallrisiko (DE-588)4205942-2 gnd rswk-swf (DE-588)4113937-9 Hochschulschrift gnd-content Kreditwesen (DE-588)4032950-1 s Kredit (DE-588)4032923-9 s Ausfallrisiko (DE-588)4205942-2 s Messung (DE-588)4038852-9 s Rating (DE-588)4255219-9 s Ökonometrisches Modell (DE-588)4043212-9 s DE-604 text/html http://www.dissertation.de/buch.php3?buch=5090 Ausführliche Beschreibung HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015919426&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Posch, Peter N. On the dynamics of credit risk an econometric analysis Ökonometrisches Modell (DE-588)4043212-9 gnd Kreditwesen (DE-588)4032950-1 gnd Rating (DE-588)4255219-9 gnd Kredit (DE-588)4032923-9 gnd Messung (DE-588)4038852-9 gnd Ausfallrisiko (DE-588)4205942-2 gnd |
| subject_GND | (DE-588)4043212-9 (DE-588)4032950-1 (DE-588)4255219-9 (DE-588)4032923-9 (DE-588)4038852-9 (DE-588)4205942-2 (DE-588)4113937-9 |
| title | On the dynamics of credit risk an econometric analysis |
| title_auth | On the dynamics of credit risk an econometric analysis |
| title_exact_search | On the dynamics of credit risk an econometric analysis |
| title_exact_search_txtP | On the dynamics of credit risk an econometric analysis |
| title_full | On the dynamics of credit risk an econometric analysis Peter N. Posch |
| title_fullStr | On the dynamics of credit risk an econometric analysis Peter N. Posch |
| title_full_unstemmed | On the dynamics of credit risk an econometric analysis Peter N. Posch |
| title_short | On the dynamics of credit risk |
| title_sort | on the dynamics of credit risk an econometric analysis |
| title_sub | an econometric analysis |
| topic | Ökonometrisches Modell (DE-588)4043212-9 gnd Kreditwesen (DE-588)4032950-1 gnd Rating (DE-588)4255219-9 gnd Kredit (DE-588)4032923-9 gnd Messung (DE-588)4038852-9 gnd Ausfallrisiko (DE-588)4205942-2 gnd |
| topic_facet | Ökonometrisches Modell Kreditwesen Rating Kredit Messung Ausfallrisiko Hochschulschrift |
| url | http://www.dissertation.de/buch.php3?buch=5090 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015919426&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT poschpetern onthedynamicsofcreditriskaneconometricanalysis |