Rating Models and Validation - Oesterreichische Nationalbank

More documents

Recommendations

Info

Rating Models and Validation II ESTIMATING AND VALIDATING PROBABILITY OF DEFAULT (PD) 1 Defining Segments for Credit Assessment Credit assessments are meant to help a bank measure whether potential borrowers will be able to meet their loan obligations in accordance with contractual agreements. However, a credit institution cannot perform credit assessments in the same way for all of its borrowers. This point is supported by three main arguments, which will be explained in greater detail below: 1. The factors relevant to creditworthiness vary for different borrower types. 2. The available data sources vary for different borrower types. 3. Credit risk levels vary for different borrower types. Ad 1. Wherever possible, credit assessment procedures must include all data and information relevant to creditworthiness. However, the factors determining creditworthiness will vary according to the type of borrower concerned, which means that it would not make sense to define a uniform data set for a bankÕs entire credit portfolio. For example, the credit quality of a government depends largely on macroeconomic indicators, while a company will be assessed on the basis of the quality of its management, among other things. Ad 2. Completely different data sources are available for various types of borrowers. For example, the bank can use the annual financial statements of companies which prepare balance sheets in order to assess their credit quality, whereas this is not possible in the case of retail customers. In the latter case, it is necessary to gather analogous data, for example by requesting information on assets and liabilities from the customers themselves. Ad 3. Empirical evidence shows that average default rates vary widely for different types of borrowers. For example, governments exhibit far lower default rates than business enterprises. Therefore, banks should account for these varying levels of risk in credit assessment by segmenting their credit portfolios accordingly. This also makes it possible to adapt the intensity of credit assessment according to the risk involved in each segment. Segmenting the credit portfolio is thus a basic prerequisite for assessing the creditworthiness of all a bankÕs borrowers based on the specific risk involved. On the basis of business considerations, we distinguish between the following general segments in practice: — Governments and the public sector — Financial service providers — Corporate customers ¥ Enterprises/business owners ¥ Specialized lending — Retail customers 8 Guidelines on Credit Risk Management
This segmentation from the business perspective is generally congruent with the regulatory categorization of assets in the IRB approach under Basel II and the draft EU directive: 1 — Sovereigns/central governments — Banks/institutions — Corporates ¥ Subsegment: Specialized lending — Retail customers — Equity Due to its highly specific characteristics, the equity segment is not discussed in detail in this document. However, as the above-mentioned general segments themselves are generally not homogeneous, a more specific segmentation is necessary (see chart 1). One conspicuous feature of our best-practice segmentation is its inclusion of product elements in the retail customer segment. In addition to borrower-specific creditworthiness factors, transaction-specific factors are also attributed importance in this segment. Further information on this special feature can be found in Section 2.5, Retail Customers, where in particular its relationship to Basel II and the draft EU directive is discussed. 1 EUROPEAN COMMISSION, draft directive on regulatory capital requirements, Article 47, No. 1—9. Rating Models and Validation Guidelines on Credit Risk Management 9
Page 1 and 2: ≈√ Guidelines on Credit Risk Ma
Page 3 and 4: The ongoing development of contempo
Page 5 and 6: 5 Developing a Rating Model 60 5.1
Page 7: Rating Models and Validation I INTR
Page 11 and 12: The best-practice segmentation pres
Page 13 and 14: Chart 2: Data Requirements for Gove
Page 15 and 16: 2.2 Financial Service Providers In
Page 17 and 18: Insurance Companies Due to their di
Page 19 and 20: Capital Market-Oriented/Internation
Page 21 and 22: — Market prospects are not assess
Page 23 and 24: Chart 5: Data Requirements for Corp
Page 25 and 26: elationships in the project, these
Page 27 and 28: Before the Project As the repayment
Page 29 and 30: Chart 6: Data Requirements for Reta
Page 31 and 32: During the Credit Term Instead of o
Page 33 and 34: 3.1 Heuristic Models Heuristic mode
Page 35 and 36: Chart 9: Information Categories for
Page 37 and 38: Explanatory Component The explanato
Page 39 and 40: This example defines linguistic ter
Page 41 and 42: ments as to whether higher or lower
Page 43 and 44: Chart 16: Indicators in the ÒCrebo
Page 45 and 46: Logistic regression has a number of
Page 47 and 48: adapts the network according to any
Page 49 and 50: The parameters required to calculat
Page 51 and 52: ture of the borrowerÕs creditworth
Page 53 and 54: Chart 24: Vertical Linking of Ratin
Page 55 and 56: e necessary in this case if the def
Page 57 and 58: 4.1.5 Consistency Heuristic models
Page 59 and 60:
Compared to heuristic models, stati
Page 61 and 62:
the data set and statistical testin
Page 63 and 64:
data collection procedure. This pro
Page 65 and 66:
full surveys is often too high, esp
Page 67 and 68:
essential structural characteristic
Page 69 and 70:
For each block, interim objectives
Page 71 and 72:
Chart 34: Creating the Analysis Dat
Page 73 and 74:
Chart 35: Creating the Analysis and
Page 75 and 76:
Once a quality-assured data set has
Page 77 and 78:
an indicator should only be used in
Page 79 and 80:
Transformation of Indicators In ord
Page 81 and 82:
the scoring functions developed usi
Page 83 and 84:
Chart 37: Significance of Quantitat
Page 85 and 86:
— For all other statistical and h
Page 87 and 88:
of approximately 10 intervals shoul
Page 89 and 90:
With regard to the time interval be
Page 91 and 92:
around the main diagonal, however,
Page 93 and 94:
tial increase in marginal default r
Page 95 and 96:
Chart 46: Aspects of Rating Model V
Page 97 and 98:
— Model development procedure Mod
Page 99 and 100:
and bad refer to whether a credit d
Page 101 and 102:
numbers of cases per class observed
Page 103 and 104:
Chart 54: Depiction of a and b erro
Page 105 and 106:
Chart 57: Shape of the a—b Error
Page 107 and 108:
Interpretation of the Pietra Index
Page 109 and 110:
The following relation applies to t
Page 111 and 112:
Chart 62: ROC Curve with Simultaneo
Page 113 and 114:
Chart 65: Interpretation of the Bay
Page 115 and 116:
The table below (chart 67) shows a
Page 117 and 118:
The lower the Brier Score is, the b
Page 119 and 120:
Chart 70 shows the reliability diag
Page 121 and 122:
Chart 71: Identification of Signifi
Page 123 and 124:
estimates, which means that it is e
Page 125 and 126:
transition matrix, the data require
Page 127 and 128:
tency of the cumulative and conditi
Page 129 and 130:
to internal back-testing results in
Page 131 and 132:
Changes in correlations One of the
Page 133 and 134:
6.4.3 Developing Stress Tests This
Page 135 and 136:
Counterparty-based and credit facil
Page 137 and 138:
tify decisive factors influencing i
Page 139 and 140:
III ESTIMATING AND VALIDATING LGD/E
Page 141 and 142:
Chart 81: Loss Components in LGD to
Page 143 and 144:
assets, as the danger exists that m
Page 145 and 146:
— Collateral: Collateral value, c
Page 147 and 148:
Chart 87: Overview of Customer Type
Page 149 and 150:
transaction, however, it is also ne
Page 151 and 152:
Implementing an in-house LGD estima
Page 153 and 154:
nents; this is analogous to the use
Page 155 and 156:
those assets which serve as collate
Page 157 and 158:
during the realization period. The
Page 159 and 160:
Chart 91: Example of Segmentation f
Page 161 and 162:
ity of the realization market and t
Page 163 and 164:
The level of utilization for off-ba
Page 165 and 166:
8.3 EAD Estimation Methods As in th
Page 167 and 168:
IV REFERENCES Backhaus, Klaus/Erich
Page 169 and 170:
Stuhlinger, Matthias, Rolle von Rat
Page 171 and 172:
Pytlik, Martin, Diskriminanzanalyse
show all

Rating Models and Validation - Oesterreichische Nationalbank

Create successful ePaper yourself

Delete template?

Save as template?