Miller, Patrick Michel (2017) Modeling and Estimating the Loss Given Default of Leasing Contracts. PhD thesis, Universität zu Köln.

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Abstract

This thesis consists of three essays dealing with the modeling and estimation of the LGD of leases. The first essay (Hartmann-Wendels, Miller, and Töws, 2014, Loss given default for leasing: Parametric and nonparametric estimations) focuses on the methodological aspects of estimating the LGD and compares different estimation approaches. The results expose that it is indispensable to interpret the results of different validation techniques in order to reliably evaluate which methods are suitable to predict the LGD of leases. Specifically, it its exposed that in-sample results might be misleading when estimating out-of-sample LGDs. It turns out that finite mixture models are capable of reproducing the shape of the LGD density, but the generated LGD predictions are quite poor. In fact, the results show that the model tree M5’ is particularly suited to predict leasing LGDs. Motivated by the insights gained in the first study (Miller, 2015, Does the Economic Situation Affect the Loss Given Default of Leases?) analyzes the factors driving the LGD of leases. The results show that the factors identified as drivers of the LGD are to some extent different for the considered companies. Nevertheless, the results affirm that the LGD of leases generally depends in particular on factors that are related to the leases asset. Based on the findings of the first two studies, the third essay (Miller and Töws, 2016, Loss Given Default-Adjusted Workout Processes for Leases) introduces an LGD estimation approach explicitly for leases. This approach bases on the economic consideration that the revenues received during the workout process of a defaulted lease come from two payment sources. The results show that the developed multi-step model generates precise and sturdy predictions of leasing LGDs. Moreover, this multi-step LGD estimation approach provides valuable interim results that can be used as a decision support for actions to be taken during the workout process of a defaulted lease.

Item Type: Thesis (PhD thesis)
Creators:
CreatorsEmailORCID
Miller, Patrick MichelP-Miller@gmx.deUNSPECIFIED
URN: urn:nbn:de:hbz:38-71214
Subjects: General statistics
Mathematics
Management and auxiliary services
Uncontrolled Keywords:
KeywordsLanguage
Loss Given Default, Leasing, Workout Process, Regression and Model Trees, Finite Mixture Models, Idiosyncratic and Macroeconomic Factors, Economic Model, Forecasting, ValidationEnglish
Faculty: Faculty of Management, Economy and Social Sciences
Divisions: Faculty of Management, Economy and Social Sciences > Department of Bank Management
Language: English
Date: 3 February 2017
Date of oral exam: 11 January 2017
Referee:
NameAcademic Title
Hartmann-Wendels, ThomasUniv.-Prof. Dr.
Hess, DieterUniv.-Prof. Dr.
Schradin, Heinrich R.Univ.-Prof. Dr.
Full Text Status: Public
Date Deposited: 01 Feb 2017 09:38
URI: http://kups.ub.uni-koeln.de/id/eprint/7121

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