Advanced Methods for Loss Given Default Estimation

This thesis consists of three essays on the estimation of the risk parameter LGD. The first essay (Hartmann-Wendels, Miller, and Töws, 2014, Loss given default for leasing: Parametric and nonparametric estimations) fills a gap in LGD related literature by focusing on elementary differences of the examined estimation approaches. We find that finite mixture models are quite capable of reproducing the unusual shape of the LGD distribution. However, out-of-sample the estimation error increases significantly. Model trees produce robust in-sample and out-of-sample estimations. Furthermore, we find that the improvement of advanced models increases with an increasing dataset. The second essay (Töws, 2014, The impact of debtor recovery on loss given default) addresses the economic consideration of the workout process of defaulted contracts. Dependent on the lessor’s workout strategy, defaulted contracts may develop in two distinct ways. Either the default reason can be dissolved and the debtor recovers or the contract must be written off. The study indicates the benefits of establishing the lessor’s expertise in assessing a defaulted contract’s continuation worthiness. If successfully implemented, the resulting workout process should produce lower LGDs than before and thereby strengthen the lenders competitiveness. The third essay (Miller and Töws, 2015, Loss given default-adjusted workout processes for leases) contributes to the LGD estimation literature considering unique features of leasing contracts. We economically account for leasing peculiarities and develop a particularly suited approach to estimate the leasing contracts’ LGD. We separate the LGD into its cash flows of the workout process of defaulted leasing contracts. We find that the estimated values of the parts of the LGD are sturdy indicators for the success of the workout process. Thus, the lessor can benefit from the consideration of both these forecasts for his actions concerning the workout process