Loss Given Default Modelling: Comparative Analysis

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

Monitoring of Credit Risk through the Cycle: Risk Indicators

The new Credit Risk Indicator (CRI) based on credit rating migration matrices is introduced. We demonstrate strong correlation between CRI and a number of defaults through several business cycles. The new model for the simulation of the annual number of defaults, based on the 1st quarter CRI data, is proposed

Monitoring of Credit Risk through the Cycle: Risk Indicators

The new Credit Risk Indicator (CRI) based on credit rating migration matrices is introduced. We demonstrate strong correlation between CRI and a number of defaults through several business cycles. The new model for the simulation of the annual number of defaults, based on the 1st quarter CRI data, is proposed

Overnight Index Rate: Model, Calibration, and Simulation

In this study the extended Overnight Index Rate (OIR) model is presented. The fitting function for the probability distribution of the OIR daily returns is based on three different Gaussian distributions which provide modelling of the narrow central peak and the wide fat-tailed component. Calibration algorithm for the model is developed and investigated using the historical OIR data

Modelling of stochastic fat-tailed auto-correlated processes: an application to short-term rates

Many financial products sensitive to daily rate changes dictate the importance of adequate modelling of short-term rates. Their intrinsic properties are investigated based on historical market data. A new short-term rate model with the non-Gaussian random driver and auto-correlation factors is introduced. Special calibration procedures for the model are presented.Short-term rate stochastic dynamics are investigated in several numerical experiments

Loss Given Default Modelling: Comparative Analysis

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

Overnight Index Rate: Model, Calibration, and Simulation

In this study the extended Overnight Index Rate (OIR) model is presented. The fitting function for the probability distribution of the OIR daily returns is based on three different Gaussian distributions which provide modelling of the narrow central peak and the wide fat-tailed component. Calibration algorithm for the model is developed and investigated using the historical OIR data

Modelling of stochastic fat-tailed auto-correlated processes: an application to short-term rates*

Abstract Many financial products sensitive to daily rate changes dictate the importance of adequate modelling of short-term rates. Their intrinsic properties are investigated based on historical market data. A new short-term rate model with the non-Gaussian random driver and auto-correlation factors is introduced. Special calibration procedures for the model are presented. Short-term rate stochastic dynamics are investigated in several numerical experiments

Modelling of stochastic fat-tailed auto-correlated processes: an application to short-term rates

Many financial products sensitive to daily rate changes dictate the importance of adequate modelling of short-term rates. Their intrinsic properties are investigated based on historical market data. A new short-term rate model with the non-Gaussian random driver and auto-correlation factors is introduced. Special calibration procedures for the model are presented. Short-term rate stochastic dynamics are investigated in several numerical experiments.