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Loss given default decomposition using mixture distributions of in-default events
- Source :
- European Journal of Operational Research. 292:1187-1199
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- Modeling loss in the case of default is a crucial task for financial institutions to support the decision making process in the risk management framework. It has become an inevitable part of modern debt collection strategies to keep promising loans on the banking book and to write off those that are not expected to be recovered at a satisfactory level. Research tends to model Loss Given Default directly or to decompose it based on the dependent variable distribution. Such an approach neglects the patterns which exist beneath the recovery process and are mainly driven by the activities made by collectors in the event of default. To overcome this problem, we propose a decomposition of the LGD model that integrates cures, partial recoveries, and write-offs into one equation, defined based on common collection strategies. Furthermore, various levels of data aggregation are applied to each component to reflect the domain that influences each stage of the default process. To assess the robustness of our approach, we propose a comparison with two benchmark models on two different datasets. We assess the goodness of fit on out-of-sample data and show that the proposed decomposition is more effective than state-of-the-art methods, maintaining a strong level of interpretability.
- Subjects :
- 050210 logistics & transportation
021103 operations research
Information Systems and Management
Write-off
Variables
General Computer Science
Event of default
Computer science
media_common.quotation_subject
05 social sciences
Risk management framework
0211 other engineering and technologies
02 engineering and technology
Management Science and Operations Research
Industrial and Manufacturing Engineering
Loss given default
Goodness of fit
Modeling and Simulation
Debt
0502 economics and business
Econometrics
Default
Robustness (economics)
media_common
Interpretability
Subjects
Details
- ISSN :
- 03772217
- Volume :
- 292
- Database :
- OpenAIRE
- Journal :
- European Journal of Operational Research
- Accession number :
- edsair.doi...........74585b48ec4b27dc52557e13279698e2