Basel III Credit Rating Systems: An Applied Guide to by Luisa Izzi, Gianluca Oricchio, Laura Vitale (auth.)

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By Luisa Izzi, Gianluca Oricchio, Laura Vitale (auth.)

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4). 5). 5 Main steps in the development of purely expert-based models Classfinal 22 QUANTITATIVE APPROACH TO CREDIT RATING MODELS defaults (called a ‘high default portfolio’)2 it is possible to adopt a statistical approach,3 in cases supported, for the assessment of qualitative information, by judgmental techniques. The most frequently adopted statistical technique for the corporate SME segment is logistic regression; alternative techniques are discriminant analysis, probit models and the more recent inductive models of a heuristic nature, such as genetic algorithms and neural networks.

10 Example of a ‘U-shaped’ factor As an example, for continuous variables, one can identify, for each indicator, the value interval [xl ; xu ], where a significant portion of observations fall (equal, for example, to 75 percent–80 percent) and, at the same time, the monotonic relation with the default event appears with specific evidence. Then, the extremes of this interval are identified – namely, the upper and lower bounds denoted, respectively, as xu and xl – and it is possible, by means of a deterministic transformation (logit, for example) to enhance the discriminatory capability of the single factor in the interval [xl ; xu ] and flatten it outside the interval, where the relation with the default is less important.

The release of models with expert-based modules aims to make known the rating discipline also for the cases of portfolios/sub-segments less relevant than others, in terms of number of positions/default rates. This contributes to settling down of a data-collection process on a systematic base on these bank portfolios. As soon as a reliable database is available for these modules, it will be possible to start the ‘objectivization’ phase of weights and variables following statistical techniques. 1 Statistical expert-based models Possible models constituted both by statistical components and by expertbased modules are devoted to the evaluation of corporate SME counterparties belonging, for example, to the segments: farmers, start-up, consortia and financial companies.

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