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2015 | 16(XVI) | nr 1 | 108--115
Tytuł artykułu

Application of Mixed Models and Families of Classifiers to Estimation of Financial Risk Parameters

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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)
  • Warsaw University of Life Sciences - SGGW, Poland
  • Warsaw University of Life Sciences - SGGW, Poland
  • Berk R. A. (2008) Statistical learning from a regression perspective, Springer, New York.
  • Bi J., Bennett K. (2003) Regression error characteristic curves, Proceedings of the 20th International Conference on Machine Learning.
  • Breiman L. (2001) Random Forests, Machine Learning, Vol. 45.
  • Calabrese R. (2012) Regression model for proportions with probability masses at zero and one. Working Paper,
  • Ferrari S. L. P., Cribari-Neto F. (2004) Beta Regression for Modeling Rates and Proportions, Journal of Applied Statistics, No. 31.
  • Hastie T., Tibshirani R., Friedman J. (2009) The elements of statistical learning. Data Mining, Inference and Prediction. Second Edition, Springer, New York.
  • Karwański M., Gostkowski M., Jałowiecki P. (2015) LGD Modeling: an application to data from a polish bank, On-line Risk Journals available on
  • Loterman G., Brown I., Martens D., Mues Ch., Baesens B. (2012) Benchmarking regression algorithms for loss given default modeling, International Journal of Forecasting, No. 28.
  • Ospina R., Ferrari S. L. P. (2012) A General Class of Zero-or-one inflated beta Regression Models, Computational Statistics and Data Analysis, No. 56.
  • Papke L, Wooldridge J. (1996) Econometric Methods for Fractional Response Variables with an Application to 401(K) Plan Participation Rate, Journal of Applied Econometrics, Vol. 11.
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