A practical approach to validating a pd model

We will contextualise the proposed methodology by applying it to a default model of mortgage loans of a commercial bank in the Netherlands. Keywords: Credit risk, Probability of default, Basel II, Statistical validation, Logit model. The capital adequacy framework Basel II aims to promote the adoption of stronger risk management practices by the banking industry.To have a valid risk estimate and allocate economic capital efficiently, a credit institution has to be sure of the adequacy of its risk measurement methods and of the estimates for the default probabilities.Additionally, the validation of rating grades is a regulatory requirement to become an internal ratings based approach bank (IRBA bank).

In backtesting, the predicted risk measurements (PD, LGD, EAD) will be contrasted with observed measurements using a workbench of available test statistics to evaluate the calibration, discrimination and stability of the model.This paper will focus on the quantitative PD validation process within a Basel II context.We will set forth a traffic light indicator approach that employs all relevant statistical tests to quantitatively validate the used PD model, and document this approach with a real-life case study.It could be something as simple as a run away script or learning how to better use E-utilities, for more efficient work such that your work does not impact the ability of other researchers to also use our site.To restore access and understand how to better interact with our site to avoid this in the future, please have your system administrator contact [email protected]

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