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Titel:

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

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Min, A.; Scherer, M.; Schischke, A.; Zagst, R.
Nicht-TUM Koautoren:
nein
Kooperation:
-
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
Zeitschriftentitel:
Mathematics
Journal gelistet in FT50 Ranking:
nein
Jahr:
2020
Band / Volume:
8
Heft / Issue:
11
Volltext / DOI:
doi:10.3390/math8111856
Status:
Verlagsversion / published
Urteilsbesprechung:
0
Key publication:
Nein
Peer reviewed:
Nein
commissioned:
not commissioned
Technology:
Nein
Interdisziplinarität:
Nein
Leitbild:
;
Ethics und Sustainability:
Nein
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