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Journal of Financial Services Research

Erschienen in:

01.08.2008

The Sensitivity of the Loss Given Default Rate to Systematic Risk: New Empirical Evidence on Bank Loans

verfasst von: Stefano Caselli, Stefano Gatti, Francesca Querci

Erschienen in: Journal of Financial Services Research | Ausgabe 1/2008

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Abstract

We verify the existence of a relation between loss given default rate (LGDR) and macroeconomic conditions by examining 11,649 bank loans concerning the Italian market. Using both the univariate and multivariate analyses, we pinpoint diverse macroeconomic explanatory variables for LGDR on loans to households and SMEs. For households, LGDR is more sensitive to the default-to-loan ratio, the unemployment rate, and household consumption. For SMEs, LGDR is influenced by the total number of employed people and the GDP growth rate. These findings corroborate the Basel Committee’s provision that LGDR quantification process must identify distinct downturn conditions for each supervisory asset class.

Nächster Artikel Information, Credit Risk, Lender Specialization and Loan Pricing: Evidence from the DIP Financing Market

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We have reclassified information contained in the data set. For each loan, we have used the code and designation applied by ISTAT (Italy’s Central Statistics Institute), thus ensuring both standard treatment of data as well as a reasonable number of clusters. In this sense, using the RAE code (the Italian acronym for branch of economic activity) would have ensured greater specificity and the creation of subgroups with more internal homogeneity. However, the resulting subgroups would have included too small a number of loans to be considered statistically significant.

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Titel: The Sensitivity of the Loss Given Default Rate to Systematic Risk: New Empirical Evidence on Bank Loans
verfasst von: Stefano Caselli
Stefano Gatti
Francesca Querci
Publikationsdatum: 01.08.2008
Verlag: Springer US
Erschienen in: Journal of Financial Services Research / Ausgabe 1/2008
Print ISSN: 0920-8550
Elektronische ISSN: 1573-0735
DOI: https://doi.org/10.1007/s10693-008-0033-8