Loss Given Default (LGD) plays an essential role in credit risk modeling. LGD measures the credit loss it can be expressed as a percentage of exposure, which is lost in the case of an event when the borrower does not fulfill his obligations (default). It is treated as a random variable with bimodal distribution.

Many methods for LGD are proposed in literature. Regression-type models using appropriate explanatory variables, named risk factors, are generally preferred as the most flexible approach. For LGD estimation advanced statistical models such as beta regression can be applied. The parametric methods, however, require  amendments of the “inflation” type that lead to mixed modeling approach. Contrary to classical statistical methods based on probability distribution, ensemble models such as gradient boosting or random forests operate with information and allow for more flexible model adjustment. The aim of the research  is to present results of LGD modeling using mixed models and compare it with ensemble approach.

Calculations were done on real data from one of the biggest Polish bank. They related to the overdrafts in the segment of Small and Medium Enterprises (SME). The overdrafts were chosen because revolving loans don’t depend on eligible collaterals or guarantees and the risk mitigation effect can be calculated using a consistent methodology which allows for pure comparison of different models.

• Legal notice:
  

  Copyright (c) Pielaszek Research, all rights reserved.
  The above materials, including auxiliary resources, are subject to Publisher's copyright and the Author(s) intellectual rights. Without limiting Author(s) rights under respective Copyright Transfer Agreement, no part of the above documents may be reproduced without the express written permission of Pielaszek Research, the Publisher. Express permission from the Author(s) is required to use the above materials for academic purposes, such as lectures or scientific presentations.
  In every case, proper references including Author(s) name(s) and URL of this webpage: https://science24.com/paper/31833 must be provided.

1. Analysis of stability of  operational risk models
2. Modeling correlations in operational risk
3. Propensity score matching and its application to risk drivers selection in financial setting
4. Application of migration matrices to risk evaluation and their effect on portfolio value
5. Influence of motherhood on research activity of women on the example of Warsaw University of Life Sciences
6. Models of rating dynamics
7. Selekcja i klasyfikacja spółek notowanych na Warszawskiej Giełdzie Papierów Wartościowych przy pomocy pakietu SAS