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Document type:
Zeitschriftenaufsatz 
Author(s):
Min, A.; Scherer, M.; Schischke, A.; Zagst, R. 
Non-TUM Co-author(s):
nein 
Cooperation:
Title:
Modeling Recovery Rates of Small- and Medium-Sized Entities in the US 
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:
Journal issue:
11 
Fulltext / DOI:
Status:
Verlagsversion / published 
Key publication:
Nein 
Peer reviewed:
Nein 
Commissioned:
not commissioned 
Technology:
Nein 
Interdisciplinarity:
Nein 
Mission statement:
Ethics and Sustainability:
Nein 
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