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Downturn LGD modeling using quantile regression

Krüger, Steffen and Rösch, Daniel (2017) Downturn LGD modeling using quantile regression. Journal of Banking & Finance 79, pp. 42-56.

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Abstract

Literature on Losses Given Default (LGD) usually focuses on mean predictions, even though losses are extremely skewed and bimodal. This paper proposes a Quantile Regression (QR) approach to get a comprehensive view on the entire probability distribution of losses. The method allows new insights on covariate effects over the whole LGD spectrum. In particular, middle quantiles are explainable by ...

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Item type:Article
Date:June 2017
Institutions:Business, Economics and Information Systems > Institut für Betriebswirtschaftslehre
Business, Economics and Information Systems > Institut für Betriebswirtschaftslehre > Lehrstuhl für Statistik und Risikomanagement (Prof. Dr. Rösch)
Research groups and research centres:Center of Finance
Identification Number:
ValueType
10.1016/j.jbankfin.2017.03.001DOI
Classification:
NotationType
G20Journal of Economics Literature Classification
G28Journal of Economics Literature Classification
C51Journal of Economics Literature Classification
Keywords:RECOVERY RATES; GIVEN-DEFAULT; BANK LOANS; Loss given default; Downturn; Quantile regression; Recovery; Validation
Dewey Decimal Classification:300 Social sciences > 330 Economics
300 Social sciences > 330 Economics
Status:Published
Refereed:Yes, this version has been refereed
Created at the University of Regensburg:Yes
Item ID:35374
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