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Article: Modeling default risk via a hidden Markov model of multiple sequences

TitleModeling default risk via a hidden Markov model of multiple sequences
Authors
KeywordsBond
Default
Expected shortfall (ES)
Hidden Markov model (HMM)
Logistic regression model
Prediction
Value-at-risk (VaR)
Issue Date2010
PublisherGaodeng Jiaoyu Chubanshe. The Journal's web site is located at http://www.springer.com/computer/journal/11704
Citation
Frontiers Of Computer Science In China, 2010, v. 4 n. 2, p. 187-195 How to Cite?
AbstractDefault risk in commercial lending is one of the major concerns of the creditors. In this article, we introduce a new hidden Markov model (HMM) with multiple observable sequences (MHMM), assuming that all the observable sequences are driven by a common hidden sequence, and utilize it to analyze default data in a network of sectors. Efficient estimation method is then adopted to estimate the model parameters. To further illustrate the advantages of MHMM, we compare the hidden risk state process obtained by MHMM with that from the traditional HMMs using credit default data. We then consider two applications of our MHMM. The calculation of two important risk measures: Value-at-risk (VaR) and expected shortfall (ES) and the prediction of global risk state. We first compare the performance of MHMM and HMM in the calculation of VaR and ES in a portfolio of default-prone bonds. A logistic regression model is then considered for the prediction of global economic risk using our MHMM with default data. Numerical results indicate our model is effective for both applications. © 2010 Higher Education Press and Springer-Verlag Berlin Heidelberg.
Persistent Identifierhttp://hdl.handle.net/10722/124814
ISSN
2011 Impact Factor: 0.266
ISI Accession Number ID
Funding AgencyGrant Number
RGC7017/07P
HKU
Hung Hing Ying Physical Sciences Research Fund
Funding Information:

A preliminary version of the paper was presented in the 2nd International Symposium on Financial Information Processing (FIP) [22]. Research supported in part by RGC Grants 7017/07P and HKU Strategic Research Theme Fund on Computational Sciences and Hung Hing Ying Physical Sciences Research Fund.

References

 

DC FieldValueLanguage
dc.contributor.authorChing, WKen_HK
dc.contributor.authorLeung, HYen_HK
dc.contributor.authorWu, Zen_HK
dc.contributor.authorJiang, Hen_HK
dc.date.accessioned2010-10-31T10:55:39Z-
dc.date.available2010-10-31T10:55:39Z-
dc.date.issued2010en_HK
dc.identifier.citationFrontiers Of Computer Science In China, 2010, v. 4 n. 2, p. 187-195en_HK
dc.identifier.issn1673-7350en_HK
dc.identifier.urihttp://hdl.handle.net/10722/124814-
dc.description.abstractDefault risk in commercial lending is one of the major concerns of the creditors. In this article, we introduce a new hidden Markov model (HMM) with multiple observable sequences (MHMM), assuming that all the observable sequences are driven by a common hidden sequence, and utilize it to analyze default data in a network of sectors. Efficient estimation method is then adopted to estimate the model parameters. To further illustrate the advantages of MHMM, we compare the hidden risk state process obtained by MHMM with that from the traditional HMMs using credit default data. We then consider two applications of our MHMM. The calculation of two important risk measures: Value-at-risk (VaR) and expected shortfall (ES) and the prediction of global risk state. We first compare the performance of MHMM and HMM in the calculation of VaR and ES in a portfolio of default-prone bonds. A logistic regression model is then considered for the prediction of global economic risk using our MHMM with default data. Numerical results indicate our model is effective for both applications. © 2010 Higher Education Press and Springer-Verlag Berlin Heidelberg.en_HK
dc.languageengen_HK
dc.publisherGaodeng Jiaoyu Chubanshe. The Journal's web site is located at http://www.springer.com/computer/journal/11704en_HK
dc.relation.ispartofFrontiers of Computer Science in Chinaen_HK
dc.subjectBonden_HK
dc.subjectDefaulten_HK
dc.subjectExpected shortfall (ES)en_HK
dc.subjectHidden Markov model (HMM)en_HK
dc.subjectLogistic regression modelen_HK
dc.subjectPredictionen_HK
dc.subjectValue-at-risk (VaR)en_HK
dc.titleModeling default risk via a hidden Markov model of multiple sequencesen_HK
dc.typeArticleen_HK
dc.identifier.emailChing, WK:wching@hku.hken_HK
dc.identifier.authorityChing, WK=rp00679en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/s11704-010-0501-9en_HK
dc.identifier.scopuseid_2-s2.0-77953358100en_HK
dc.identifier.hkuros171833en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77953358100&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume4en_HK
dc.identifier.issue2en_HK
dc.identifier.spage187en_HK
dc.identifier.epage195en_HK
dc.identifier.isiWOS:000292504200005-
dc.publisher.placeChinaen_HK
dc.identifier.scopusauthoridChing, WK=13310265500en_HK
dc.identifier.scopusauthoridLeung, HY=24780941800en_HK
dc.identifier.scopusauthoridWu, Z=35209289200en_HK
dc.identifier.scopusauthoridJiang, H=55017654000en_HK
dc.identifier.citeulike7249552-

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