Książka Loss Given Default Modeling: a Comparative Analysis Olga Yashkir

Loss Given Default Modeling: a Comparative Analysis

Język: Angielski
Oprawa: Miękka
Dostępność: Dostępna u dostawcy
Wysyłamy za 5-8 dni
106.50
Several most popular Loss Given Default (LGD) models were investigated (LSM, Tobit, Three-Tiered Tob...

Informacje o książce

Język
Angielski
Oprawa
Książka - Miękka
Data wydania
2017
strony
52
EAN
9786202093750
Enbook ID
18715689
Waga
96
Wymiary
150 x 220 x 3

Pełny opis

Several most popular Loss Given Default (LGD) models were investigated (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 of explanatory variables, but not on the fitting model. Model factors were chosen based on 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.

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