Rating Models and Validation - Oesterreichische Nationalbank

More documents

Recommendations

Info

Rating Models and Validation Chart 3: Data Requirements for Financial Service Providers 16 Guidelines on Credit Risk Management
Insurance Companies Due to their different business orientation, insurance companies have to be assessed using different creditworthiness criteria from those used for credit institutions. However, the existing similarities between these institutions mean that many of the same information categories also apply to insurers. Financial institutions Financial institutions, or Òother financial service providers,Ó are similar to credit institutions. However, the specific credit assessment criteria taken into consideration may be different for financial institutions. For example, asset management companies which only act as advisors and intermediaries but to do not grant loans themselves will have an entirely different risk structure to that of credit institutions. Such differences should be taken into consideration in the different credit assessment procedures for the subsegments within the Òfinancial service providersÓ segment. However, it is not absolutely necessary to develop an entirely new rating procedure for financial institutions. Instead, it may be sufficient to use an adapted version of the rating model applied to credit institutions. It may also be possible to assess certain financial institutions with a modified corporate customer rating model, which would change the data requirements accordingly. 2.3 Corporate Customers — Enterprises/Business Owners The general segment ÒCorporate Customers — Enterprises/Business OwnersÓ can be subdivided into the following subsegments: — Capital market-oriented 2 /international companies — Other companies which prepare balance sheets — Businesses and independent professionals (not preparing balance sheets) — Small businesses — Start-ups — NPOs (non-profit organizations) The first four subsegments consist of enterprises which have already been on the market for some time. These enterprises differ in size and thus also in terms of the available data categories. In the case of start-ups, the information available will be very depending on the enterpriseÕs current stage of development and should be taken into account accordingly. The main differentiating criterion in the case of NPOs is the fact that they are not operated for the purpose of making a profit. Moreover, it is common practice in the corporate segment to develop separate rating models for various countries and regions (e.g. for enterprises in CEE countries). Among other things, these models take the accounting standards applicable in individual countries into consideration. 2 Capital market-oriented means that the company funds itself (at least in part) by means of capital market instruments (stocks, bonds, securitization). Rating Models and Validation Guidelines on Credit Risk Management 17
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 and 8: Rating Models and Validation I INTR
Page 9 and 10: This segmentation from the business
Page 11 and 12: The best-practice segmentation pres
Page 13 and 14: Chart 2: Data Requirements for Gove
Page 15: 2.2 Financial Service Providers In
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?