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Credit line exposure at default modelling using Bayesian mixed effect quantile regression

Credit line exposure at default modelling using Bayesian mixed effect quantile regression

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2758076725

Credit line exposure at default modelling using Bayesian mixed effect quantile regression

About this item

Full title

Credit line exposure at default modelling using Bayesian mixed effect quantile regression

Publisher

Hoboken, NJ: Wiley

Journal title

Journal of the Royal Statistical Society. Series A, Statistics in society, 2022-10, Vol.185 (4), p.2035-2072

Language

English

Formats

Publication information

Publisher

Hoboken, NJ: Wiley

More information

Scope and Contents

Contents

For banks, credit lines play an important role exposing both liquidity and credit risk. In the advanced internal ratings‐based approach, banks are obliged to use their own estimates of exposure at default using credit conversion factors. For volatile segments, additional downturn estimates are required. Using the world's largest database of default...

Alternative Titles

Full title

Credit line exposure at default modelling using Bayesian mixed effect quantile regression

Authors, Artists and Contributors

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_proquest_journals_2758076725

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2758076725

Other Identifiers

ISSN

1467-985X,0964-1998

E-ISSN

1467-985X

DOI

10.1111/rssa.12855

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