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Article: Extracting information from spot interest rates and credit ratings using double higher-order hidden Markov models

TitleExtracting information from spot interest rates and credit ratings using double higher-order hidden Markov models
Authors
KeywordsCredit Ratings
Double Higher-Order Hidden Markov Model
Long Range Dependence
Optimal Hidden Economic States
Spot Interest Rates
Issue Date2005
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, 2005, v. 26 n. 3-4, p. 69-102 How to Cite?
AbstractEstimating and forecasting the unobservable states of an economy are important and practically relevant topics in economics. Central bankers and regulators can use information about the market expectations on the hidden states of the economy as a reference for decision and policy makings, for instance, deciding monetary policies. Spot interest rates and credit ratings of bonds contain important information about the hidden sequence of the states of the economy. In this paper, we develop double higher-order hidden Markov chain models (DHHMMs) for extracting information about the hidden sequence of the states of an economy from the spot interest rates and credit ratings of bonds. We consider a discrete-state model described by DHHMMs and focus on the qualitative aspect of the unobservable states of the economy. The observable spot interest rates and credit ratings of bonds depend on the hidden states of the economy which are modelled by DHHMMs. The DHHMMs can incorporate the persistent phenomena of the time series of spot interest rates and the credit ratings. We employ the maximum likelihood method and the EM algorithm, namely Viterbi's algorithm, to uncover the optimal hidden sequence of the states of the economy which can be interpreted the "best" estimate of the sequence of the underlying economic states generating the spot interest rates and credit ratings of the bonds. Then, we develop an efficient maximum likelihood estimation method to estimate the unknown parameters in our model. Numerical experiment will be conducted to illustrate the implementation of the model. © Springer Science+Business Media, Inc. 2005.
Persistent Identifierhttp://hdl.handle.net/10722/156160
ISSN
2015 Impact Factor: 0.691
2015 SCImago Journal Rankings: 0.240
References
Errata

 

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.citationComputational Economics, 2005, v. 26 n. 3-4, p. 69-102en_US
dc.identifier.issn0927-7099en_US
dc.identifier.urihttp://hdl.handle.net/10722/156160-
dc.description.abstractEstimating and forecasting the unobservable states of an economy are important and practically relevant topics in economics. Central bankers and regulators can use information about the market expectations on the hidden states of the economy as a reference for decision and policy makings, for instance, deciding monetary policies. Spot interest rates and credit ratings of bonds contain important information about the hidden sequence of the states of the economy. In this paper, we develop double higher-order hidden Markov chain models (DHHMMs) for extracting information about the hidden sequence of the states of an economy from the spot interest rates and credit ratings of bonds. We consider a discrete-state model described by DHHMMs and focus on the qualitative aspect of the unobservable states of the economy. The observable spot interest rates and credit ratings of bonds depend on the hidden states of the economy which are modelled by DHHMMs. The DHHMMs can incorporate the persistent phenomena of the time series of spot interest rates and the credit ratings. We employ the maximum likelihood method and the EM algorithm, namely Viterbi's algorithm, to uncover the optimal hidden sequence of the states of the economy which can be interpreted the "best" estimate of the sequence of the underlying economic states generating the spot interest rates and credit ratings of the bonds. Then, we develop an efficient maximum likelihood estimation method to estimate the unknown parameters in our model. Numerical experiment will be conducted to illustrate the implementation of the model. © Springer Science+Business Media, Inc. 2005.en_US
dc.languageengen_US
dc.publisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0927-7099en_US
dc.relation.ispartofComputational Economicsen_US
dc.subjectCredit Ratingsen_US
dc.subjectDouble Higher-Order Hidden Markov Modelen_US
dc.subjectLong Range Dependenceen_US
dc.subjectOptimal Hidden Economic Statesen_US
dc.subjectSpot Interest Ratesen_US
dc.titleExtracting information from spot interest rates and credit ratings using double higher-order hidden Markov modelsen_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.doi10.1007/s10614-005-9010-6en_US
dc.identifier.scopuseid_2-s2.0-33645754983en_US
dc.identifier.hkuros116041-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33645754983&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume26en_US
dc.identifier.issue3-4en_US
dc.identifier.spage69en_US
dc.identifier.epage102en_US
dc.publisher.placeUnited Statesen_US
dc.relation.erratumdoi:10.1007/s10614-006-9057-z-
dc.identifier.scopusauthoridSiu, TK=8655758200en_US
dc.identifier.scopusauthoridChing, WK=13310265500en_US
dc.identifier.scopusauthoridFung, ES=36886537700en_US
dc.identifier.scopusauthoridNg, MK=34571761900en_US
dc.identifier.citeulike586696-

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