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Multi-level factor analysis of bond risk premia

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dc.contributor.authorKim, Dukpa-
dc.contributor.authorKim, Yunjung-
dc.contributor.authorBak, Yuhyeon-
dc.date.accessioned2021-09-02T22:11:12Z-
dc.date.available2021-09-02T22:11:12Z-
dc.date.created2021-06-18-
dc.date.issued2017-12-
dc.identifier.issn1081-1826-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/81257-
dc.description.abstractEarlier studies in the finance literature show that macroeconomic fundamentals can predict excess bond returns. We employ a multi-level factor model to estimate global and sectoral factors separately and show that (i) the real factors possess most important predictive power existing in the panel; (ii) the financial factors might have some predictive power but less than the real factors; (iii) the inflation factors have almost no predictive power and (iv) the excess bond returns have a countercyclical component.-
dc.languageEnglish-
dc.language.isoen-
dc.publisherWALTER DE GRUYTER GMBH-
dc.subjectFACTOR MODELS-
dc.subjectUNCERTAINTY-
dc.titleMulti-level factor analysis of bond risk premia-
dc.typeArticle-
dc.contributor.affiliatedAuthorKim, Dukpa-
dc.identifier.doi10.1515/snde-2015-0080-
dc.identifier.scopusid2-s2.0-85037832338-
dc.identifier.wosid000418083900004-
dc.identifier.bibliographicCitationSTUDIES IN NONLINEAR DYNAMICS AND ECONOMETRICS, v.21, no.5-
dc.relation.isPartOfSTUDIES IN NONLINEAR DYNAMICS AND ECONOMETRICS-
dc.citation.titleSTUDIES IN NONLINEAR DYNAMICS AND ECONOMETRICS-
dc.citation.volume21-
dc.citation.number5-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalRegisteredClassssci-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaBusiness & Economics-
dc.relation.journalResearchAreaMathematical Methods In Social Sciences-
dc.relation.journalWebOfScienceCategoryEconomics-
dc.relation.journalWebOfScienceCategorySocial Sciences, Mathematical Methods-
dc.subject.keywordPlusFACTOR MODELS-
dc.subject.keywordPlusUNCERTAINTY-
dc.subject.keywordAuthorcommon factors-
dc.subject.keywordAuthorexcess bond returns-
dc.subject.keywordAuthorpredictive regression-
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