Loss Given Default Modeling: a Comparative Analysis

ISBN-13: 9786202093750 / Angielski / Miękka / 2017 / 52 str.

Olga Yashkir; Yuri Yashkir

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.

O autorze

Olga Yashkir held Ph.D. in mathematics and physics. She has spent last 18 years working in bank risk management in Bank of Montreal, TD Bank, Deutsche Bank, and Barclays Bank as Senior Quontitative Analyst and Vice President. She published more than 30 papers on stochastic optimization, stochastic rate models, and credit risk factors.