Estimation of banking technology under credit uncertainty
Credit risk is crucial to understanding banks’ production technology and should be explicitly accounted for when modeling the latter. The banking literature has largely accounted for risk usingex-post realizations of banks’ uncertain outputs and the variables intended to capture risk. This is equiva...
- Autores:
-
Malikov, Emir
Restrepo-Tobón, Diego
Kumbhakar, Subal C.
- Tipo de recurso:
- Fecha de publicación:
- 2014
- Institución:
- Universidad EAFIT
- Repositorio:
- Repositorio EAFIT
- Idioma:
- eng
- OAI Identifier:
- oai:repository.eafit.edu.co:10784/7619
- Acceso en línea:
- http://hdl.handle.net/10784/7619
- Palabra clave:
- Ex-ante cost function
Production uncertainty
Productivity
Returns to scale
Risk
- Rights
- License
- restrictedAccess
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20142015-11-06T21:15:36Z20142015-11-06T21:15:36Z0377-7332http://hdl.handle.net/10784/761910.1007/s00181-014-0849-zCredit risk is crucial to understanding banks’ production technology and should be explicitly accounted for when modeling the latter. The banking literature has largely accounted for risk usingex-post realizations of banks’ uncertain outputs and the variables intended to capture risk. This is equivalent to estimating an ex-post realization of bank’s production technology which, however, may not reflect optimality conditions that banks seek to satisfy under uncertainty. The ex-post estimates of technology are likely to be biased and inconsistent, and one thus may call into question the reliability of the results regarding banks’ technological characteristics broadly reported in the literature. However, the extent to which these concerns are relevant for policy analysis is an empirical question. In this paper, we offer an alternative methodology to estimate banks’ production technology based on the ex-ante cost function. We model credit uncertainty explicitly by recognizing that bank managers minimize costs subject to given expected outputs and credit risk. We estimate unobservable expected outputs and associated credit risk levels from banks’ supply functions via nonparametric kernel methods. We apply this framework to estimate production technology of U.S. commercial banks during the period from 2001 to 2010 and contrast the new estimates with those based on the ex-post models widely employed in the literature.engSpringer International PublishingEmpirical Economics . Vol. 49, (1), 2014, pp.185-221http://link.springer.com/article/10.1007%2Fs00181-014-0849-zhttp://link.springer.com/article/10.1007%2Fs00181-014-0849-zrestrictedAccess© Springer International Publishing AG, Part of Springer Science+Business MediaAcceso restringidohttp://purl.org/coar/access_right/c_16ecEmpirical Economics . Vol. 49, (1), 2014, pp.185-221Estimation of banking technology under credit uncertaintyarticleinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículopublishedVersionObra publicadahttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Ex-ante cost functionProduction uncertaintyProductivityReturns to scaleRiskEconomía y FinanzasFinanzasMalikov, EmirRestrepo-Tobón, DiegoKumbhakar, Subal C.Department of Economics, State University of New York at Binghamton, Department of Economics, St. Lawrence UniversityDepartment of Finance, EAFIT UniversityDepartment of Economics, State University of New York at BinghamtonGrupo de Investigación Finanzas y BancaEmpirical Economics491185221ORIGINALs00181-014-0849-z.pdfs00181-014-0849-z.pdfapplication/pdf1194915https://repository.eafit.edu.co/bitstreams/ffe389f9-4ab7-4320-ac63-bfda5464f02e/download2d3d7e00746012d79b3db5e365ca0d2cMD5110784/7619oai:repository.eafit.edu.co:10784/76192023-03-15 08:27:07.039open.accesshttps://repository.eafit.edu.coRepositorio Institucional Universidad EAFITrepositorio@eafit.edu.co |
dc.title.eng.fl_str_mv |
Estimation of banking technology under credit uncertainty |
title |
Estimation of banking technology under credit uncertainty |
spellingShingle |
Estimation of banking technology under credit uncertainty Ex-ante cost function Production uncertainty Productivity Returns to scale Risk |
title_short |
Estimation of banking technology under credit uncertainty |
title_full |
Estimation of banking technology under credit uncertainty |
title_fullStr |
Estimation of banking technology under credit uncertainty |
title_full_unstemmed |
Estimation of banking technology under credit uncertainty |
title_sort |
Estimation of banking technology under credit uncertainty |
dc.creator.fl_str_mv |
Malikov, Emir Restrepo-Tobón, Diego Kumbhakar, Subal C. |
dc.contributor.department.spa.fl_str_mv |
Economía y Finanzas Finanzas |
dc.contributor.author.spa.fl_str_mv |
Malikov, Emir Restrepo-Tobón, Diego Kumbhakar, Subal C. |
dc.contributor.affiliation.spa.fl_str_mv |
Department of Economics, State University of New York at Binghamton, Department of Economics, St. Lawrence University Department of Finance, EAFIT University Department of Economics, State University of New York at Binghamton |
dc.contributor.program.spa.fl_str_mv |
Grupo de Investigación Finanzas y Banca |
dc.subject.keyword.eng.fl_str_mv |
Ex-ante cost function Production uncertainty Productivity Returns to scale Risk |
topic |
Ex-ante cost function Production uncertainty Productivity Returns to scale Risk |
description |
Credit risk is crucial to understanding banks’ production technology and should be explicitly accounted for when modeling the latter. The banking literature has largely accounted for risk usingex-post realizations of banks’ uncertain outputs and the variables intended to capture risk. This is equivalent to estimating an ex-post realization of bank’s production technology which, however, may not reflect optimality conditions that banks seek to satisfy under uncertainty. The ex-post estimates of technology are likely to be biased and inconsistent, and one thus may call into question the reliability of the results regarding banks’ technological characteristics broadly reported in the literature. However, the extent to which these concerns are relevant for policy analysis is an empirical question. In this paper, we offer an alternative methodology to estimate banks’ production technology based on the ex-ante cost function. We model credit uncertainty explicitly by recognizing that bank managers minimize costs subject to given expected outputs and credit risk. We estimate unobservable expected outputs and associated credit risk levels from banks’ supply functions via nonparametric kernel methods. We apply this framework to estimate production technology of U.S. commercial banks during the period from 2001 to 2010 and contrast the new estimates with those based on the ex-post models widely employed in the literature. |
publishDate |
2014 |
dc.date.issued.none.fl_str_mv |
2014 |
dc.date.available.none.fl_str_mv |
2015-11-06T21:15:36Z |
dc.date.accessioned.none.fl_str_mv |
2015-11-06T21:15:36Z |
dc.date.none.fl_str_mv |
2014 |
dc.type.eng.fl_str_mv |
article info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 http://purl.org/coar/resource_type/c_2df8fbb1 |
dc.type.local.spa.fl_str_mv |
Artículo |
dc.type.hasVersion.eng.fl_str_mv |
publishedVersion |
dc.type.hasVersion.spa.fl_str_mv |
Obra publicada |
status_str |
publishedVersion |
dc.identifier.issn.none.fl_str_mv |
0377-7332 |
dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/10784/7619 |
dc.identifier.doi.none.fl_str_mv |
10.1007/s00181-014-0849-z |
identifier_str_mv |
0377-7332 10.1007/s00181-014-0849-z |
url |
http://hdl.handle.net/10784/7619 |
dc.language.iso.eng.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.spa.fl_str_mv |
Empirical Economics . Vol. 49, (1), 2014, pp.185-221 |
dc.relation.isversionof.none.fl_str_mv |
http://link.springer.com/article/10.1007%2Fs00181-014-0849-z |
dc.relation.uri.none.fl_str_mv |
http://link.springer.com/article/10.1007%2Fs00181-014-0849-z |
dc.rights.eng.fl_str_mv |
restrictedAccess |
dc.rights.spa.fl_str_mv |
© Springer International Publishing AG, Part of Springer Science+Business Media |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_16ec |
dc.rights.local.spa.fl_str_mv |
Acceso restringido |
rights_invalid_str_mv |
restrictedAccess © Springer International Publishing AG, Part of Springer Science+Business Media Acceso restringido http://purl.org/coar/access_right/c_16ec |
dc.publisher.eng.fl_str_mv |
Springer International Publishing |
dc.source.spa.fl_str_mv |
Empirical Economics . Vol. 49, (1), 2014, pp.185-221 |
institution |
Universidad EAFIT |
bitstream.url.fl_str_mv |
https://repository.eafit.edu.co/bitstreams/ffe389f9-4ab7-4320-ac63-bfda5464f02e/download |
bitstream.checksum.fl_str_mv |
2d3d7e00746012d79b3db5e365ca0d2c |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 |
repository.name.fl_str_mv |
Repositorio Institucional Universidad EAFIT |
repository.mail.fl_str_mv |
repositorio@eafit.edu.co |
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1814110663728955392 |