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Article: Modeling default data via an interactive hidden markov model

TitleModeling default data via an interactive hidden markov model
Authors
KeywordsBinomial expansion technique
Default data
Feedback effect
Hidden Markov model (HMM)
Interactive hidden Markov model (IHMM)
Issue Date2009
PublisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0927-7099
Citation
Computational Economics, 2009, v. 34 n. 1, p. 1-19 How to Cite?
AbstractIn this paper, we first introduce the use of an interactive hidden Markov model (IHMM) for modeling and analyzing default data in a sector. Under the IHMM, transitions of the hidden risk states of the sector depend on the observed number of bonds in the sector that default in the current time period. This incorporates the feedback effect of the number of defaults on the transitions of the hidden risk states. This feature seems to be more realistic and does not enjoy by the traditional HMMs. We then develop a "dynamic" version of the binomial expansion technique (BET) modulated by the IHMM for modeling the occurrence of defaults of bonds issued by firms in the same sector. Under the BET modulated by the IHMM, the number of bonds defaulting in each time period follows a Markov-modulated binomial distribution with the probability of defaulting of each bond depending on the states of the IHMM, which represent the hidden risk states of the sector. Efficient method will be presented for estimating the model parameters in the BET modulated by the IHMM. We shall compare the hidden risk state process extracted from the IHMM-modulated BET with that extracted from the BET modulated by HMM in order to illustrate the significance of the feedback effect using real data. We shall also present the estimation results for the BET modulated by the IHMM and compare them with those for the BET modulated by the HMM. © Springer Science+Business Media, LLC. 2009.
Persistent Identifierhttp://hdl.handle.net/10722/75122
ISSN
2015 Impact Factor: 0.691
2015 SCImago Journal Rankings: 0.240
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChing, WKen_HK
dc.contributor.authorSiu, TKen_HK
dc.contributor.authorLi, LMen_HK
dc.contributor.authorLi, Ten_HK
dc.contributor.authorLi, WKen_HK
dc.date.accessioned2010-09-06T07:08:07Z-
dc.date.available2010-09-06T07:08:07Z-
dc.date.issued2009en_HK
dc.identifier.citationComputational Economics, 2009, v. 34 n. 1, p. 1-19en_HK
dc.identifier.issn0927-7099en_HK
dc.identifier.urihttp://hdl.handle.net/10722/75122-
dc.description.abstractIn this paper, we first introduce the use of an interactive hidden Markov model (IHMM) for modeling and analyzing default data in a sector. Under the IHMM, transitions of the hidden risk states of the sector depend on the observed number of bonds in the sector that default in the current time period. This incorporates the feedback effect of the number of defaults on the transitions of the hidden risk states. This feature seems to be more realistic and does not enjoy by the traditional HMMs. We then develop a "dynamic" version of the binomial expansion technique (BET) modulated by the IHMM for modeling the occurrence of defaults of bonds issued by firms in the same sector. Under the BET modulated by the IHMM, the number of bonds defaulting in each time period follows a Markov-modulated binomial distribution with the probability of defaulting of each bond depending on the states of the IHMM, which represent the hidden risk states of the sector. Efficient method will be presented for estimating the model parameters in the BET modulated by the IHMM. We shall compare the hidden risk state process extracted from the IHMM-modulated BET with that extracted from the BET modulated by HMM in order to illustrate the significance of the feedback effect using real data. We shall also present the estimation results for the BET modulated by the IHMM and compare them with those for the BET modulated by the HMM. © Springer Science+Business Media, LLC. 2009.en_HK
dc.languageengen_HK
dc.publisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0927-7099en_HK
dc.relation.ispartofComputational Economicsen_HK
dc.rightsThe original publication is available at www.springerlink.com-
dc.subjectBinomial expansion techniqueen_HK
dc.subjectDefault dataen_HK
dc.subjectFeedback effecten_HK
dc.subjectHidden Markov model (HMM)en_HK
dc.subjectInteractive hidden Markov model (IHMM)en_HK
dc.titleModeling default data via an interactive hidden markov modelen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0927-7099&volume=34&issue=1&spage=1&epage=19&date=2009&atitle=Modeling+default+data+via+an+interactive+hidden+markov+modelen_HK
dc.identifier.emailChing, WK: wching@hku.hken_HK
dc.identifier.emailLi, WK: hrntlwk@hku.hken_HK
dc.identifier.authorityChing, WK=rp00679en_HK
dc.identifier.authorityLi, WK=rp00741en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/s10614-009-9183-5en_HK
dc.identifier.scopuseid_2-s2.0-66649117482en_HK
dc.identifier.hkuros167671en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-66649117482&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume34en_HK
dc.identifier.issue1en_HK
dc.identifier.spage1en_HK
dc.identifier.epage19en_HK
dc.identifier.isiWOS:000268056400001-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridChing, WK=13310265500en_HK
dc.identifier.scopusauthoridSiu, TK=8655758200en_HK
dc.identifier.scopusauthoridLi, LM=35329863000en_HK
dc.identifier.scopusauthoridLi, T=10143243300en_HK
dc.identifier.scopusauthoridLi, WK=14015971200en_HK
dc.identifier.citeulike4846868-

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