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Article: On a multivariate Markov chain model for credit risk measurement

TitleOn a multivariate Markov chain model for credit risk measurement
Authors
KeywordsCorrelated Credit Migrations
Credibility Theory
Linear Programming
Transition Matrices
Issue Date2005
PublisherRoutledge. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/14697688.asp
Citation
Quantitative Finance, 2005, v. 5 n. 6, p. 543-556 How to Cite?
AbstractIn this paper, we use credibility theory to estimate credit transition matrices in a multivariate Markov chain model for credit rating. A transition matrix is estimated by a linear combination of the prior estimate of the transition matrix and the empirical transition matrix. These estimates can be easily computed by solving a set of linear programming (LP) problems. The estimation procedure can be implemented easily on Excel spreadsheets without requiring much computational effort and time. The number of parameters is O(s 2m 2), where s is the dimension of the categorical time series for credit ratings and m is the number of possible credit ratings for a security. Numerical evaluations of credit risk measures based on our model are presented.
Persistent Identifierhttp://hdl.handle.net/10722/156159
ISSN
2015 Impact Factor: 0.794
2015 SCImago Journal Rankings: 0.565
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorSiu, TKen_US
dc.contributor.authorChing, WKen_US
dc.contributor.authorFung, ESen_US
dc.contributor.authorNg, MKen_US
dc.date.accessioned2012-08-08T08:40:39Z-
dc.date.available2012-08-08T08:40:39Z-
dc.date.issued2005en_US
dc.identifier.citationQuantitative Finance, 2005, v. 5 n. 6, p. 543-556en_US
dc.identifier.issn1469-7688en_US
dc.identifier.urihttp://hdl.handle.net/10722/156159-
dc.description.abstractIn this paper, we use credibility theory to estimate credit transition matrices in a multivariate Markov chain model for credit rating. A transition matrix is estimated by a linear combination of the prior estimate of the transition matrix and the empirical transition matrix. These estimates can be easily computed by solving a set of linear programming (LP) problems. The estimation procedure can be implemented easily on Excel spreadsheets without requiring much computational effort and time. The number of parameters is O(s 2m 2), where s is the dimension of the categorical time series for credit ratings and m is the number of possible credit ratings for a security. Numerical evaluations of credit risk measures based on our model are presented.en_US
dc.languageengen_US
dc.publisherRoutledge. The Journal's web site is located at http://www.tandf.co.uk/journals/titles/14697688.aspen_US
dc.relation.ispartofQuantitative Financeen_US
dc.subjectCorrelated Credit Migrationsen_US
dc.subjectCredibility Theoryen_US
dc.subjectLinear Programmingen_US
dc.subjectTransition Matricesen_US
dc.titleOn a multivariate Markov chain model for credit risk measurementen_US
dc.typeArticleen_US
dc.identifier.emailChing, WK:wching@hku.hken_US
dc.identifier.authorityChing, WK=rp00679en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1080/14697680500383714en_US
dc.identifier.scopuseid_2-s2.0-33644909922en_US
dc.identifier.hkuros114575-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33644909922&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume5en_US
dc.identifier.issue6en_US
dc.identifier.spage543en_US
dc.identifier.epage556en_US
dc.identifier.isiWOS:000234488900005-
dc.publisher.placeUnited Kingdomen_US
dc.identifier.scopusauthoridSiu, TK=8655758200en_US
dc.identifier.scopusauthoridChing, WK=13310265500en_US
dc.identifier.scopusauthoridFung, ES=36886537700en_US
dc.identifier.scopusauthoridNg, MK=34571761900en_US

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