Application of Mixed Models and Families of Classifiers to Estimation of Financial Risk Parameters
The essential role in credit risk modeling is Loss Given Default (LGD) estimation. LGD is treated as a random variable with bimodal distribution. For LGD estimation advanced statistical models such as beta regression can be applied. Unfortunately, the parametric methods require amendments of the "inflation" type that lead to mixed modeling approach. Contrary to classical statistical methods based on probability distribution, the families of classifiers such as gradient boosting or random forests operate with information and allow for more flexible model adjustment. The problem encountered is comparison of obtained results. The aim of the paper is to present and compare results of LGD modeling using statistical methods and data mining approach. Calculations were done on real life data sourced from one of Polish large banks.(original abstract)
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