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Title:

Modeling Recovery Rates of Small- and Medium-Sized Entities in the US

Document type:
Zeitschriftenaufsatz
Author(s):
Min, A.; Scherer, M.; Schischke, A.; Zagst, R.
Non-TUM Co-author(s):
nein
Cooperation:
-
Abstract:
A sound statistical model for recovery rates is required for various applications in quantitative risk management. We compare different models for predicting the recovery rate on borrower level including linear and quantile regressions, decision trees, neural networks and mixture regression models. We fit and apply these models on the worldwide largest loss and recovery dataset for commercial loans provided by Global Credit Data, where we focus on small- and medium-sized entities in the US. Addi...     »
Intellectual Contribution:
Discipline-based Research
Journal title:
Mathematics
Journal listet in FT50 ranking:
nein
Year:
2020
Journal volume:
8
Journal issue:
11
Fulltext / DOI:
doi:10.3390/math8111856
Status:
Verlagsversion / published
Judgement review:
0
Key publication:
Nein
Peer reviewed:
Nein
Commissioned:
not commissioned
Technology:
Nein
Interdisciplinarity:
Nein
Mission statement:
;
Ethics and Sustainability:
Nein
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