Loss Given Default Modelling: Comparative Analysis

Olga Yashkir and Yuriy Yashkir

MPRA Paper from University Library of Munich, Germany

Abstract: In this study we investigated several most popular Loss Given Default (LGD) models (LSM, Tobit, Three-Tiered Tobit, Beta Regression, Inflated Beta Regression, Censored Gamma Regression) in order to compare their performance. We show that for a given input data set, the quality of the model calibration depends mainly on the proper choice (and availability) of explanatory variables (model factors), but not on the fitting model. Model factors were chosen based on the amplitude of their correlation with historical LGDs of the calibration data set. Numerical values of non-quantitative parameters (industry, ranking, type of collateral) were introduced as their LGD average. We show that different debt instruments depend on different sets of model factors (from three factors for Revolving Credit or for Subordinated Bonds to eight factors for Senior Secured Bonds). Calibration of LGD models using distressed business cycle periods provide better fit than data from total available time span. Calibration algorithms and details of their realization using the R statistical package are presented. We demonstrate how LGD models can be used for stress testing. The results of this study can be of use to risk managers concerned with the Basel accord compliance.

Keywords: LGD; Credit Risk; LGD model; Linear regression; Tobit model; Stress testing (search for similar items in EconPapers)
JEL-codes: G14 G17 G19 G24 (search for similar items in EconPapers)
Date: 2013-03-27
New Economics Papers: this item is included in nep-ban and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

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