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The retail credit risk management is generally based on models that try to discriminate between good (people that probably will be able to pay back the debt) and bad customers (people that probably will not). Particularly, as the question explicitly asks for, you want to some references to allow to decide which customers, already acquired, to keep and which ...


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The choice of normalization depends on your data set: Without normalization : variable with high variance will have more impact on the PCA. You will have size effects. For exemple if you have one variable in meters and the other one in kilometers the one in meters will have way more impact. To avoid that you can normalize but now every variable will have ...


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There exist 3 kind of models for credit portfolio management: Structural models (as, for instance, the KMV's based-models or credit-metrics models); Actuarial (or intensity) models; Macro-Factors (or econometrics) models; I suggest you to read Derbali (2012), that's a simple paper that explains the main features and the differences among those kinds of ...


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There are 2 issues that come to mind What is the correct definition of semi-covariance $$ \frac{1}{n}\sum\limits_{i = 1}^n {\sum\limits_{j = 1}^n {\min \left( {{r_i},0} \right)} } \min \left( {{r_j},0} \right) $$ $$ \frac{1}{n}\sum\limits_{i = 1}^n {\sum\limits_{j = 1}^n {\min \left( {{r_i}{r_j},0} \right)} } $$ 2. Can you get a positive ...


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Looking at your code, you seem to be mixing the risk minimization formulation of the mean-variance problem with the risk aversion formulation. Both formulations include the "budget" constraint, that the sum of the weights equal 1, and can require that each of the weights be greater than zero, the "long-only" inequality constraints. In the risk minimization ...



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