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Article: Functional dynamic factor models with application to yield curve forecasting

TitleFunctional dynamic factor models with application to yield curve forecasting
Authors
KeywordsFunctional data analysis
Expectation maximization algorithm
Cross-validation
Roughness penalty
Natural cubic splines
Issue Date2012
Citation
Annals of Applied Statistics, 2012, v. 6, n. 3, p. 870-893 How to Cite?
AbstractAccurate forecasting of zero coupon bond yields for a continuum of maturities is paramount to bond portfolio management and derivative security pricing. Yet a universal model for yield curve forecasting has been elusive, and prior attempts often resulted in a trade-off between goodness of fit and consistency with economic theory. To address this, herein we propose a novel formulation which connects the dynamic factor model (DFM) framework with concepts from functional data analysis: a DFM with functional factor loading curves. This results in a model capable of forecasting functional time series. Further, in the yield curve context we show that the model retains economic interpretation. Model estimation is achieved through an expectation- maximization algorithm, where the time series parameters and factor loading curves are simultaneously estimated in a single step. Efficient computing is implemented and a data-driven smoothing parameter is nicely incorporated. We show that our model performs very well on forecasting actual yield data compared with existing approaches, especially in regard to profit-based assessment for an innovative trading exercise. We further illustrate the viability of our model to applications outside of yield forecasting.
Persistent Identifierhttp://hdl.handle.net/10722/219682
ISSN
2015 Impact Factor: 1.432
2015 SCImago Journal Rankings: 1.533

 

DC FieldValueLanguage
dc.contributor.authorHays, Spencer-
dc.contributor.authorShen, Haipeng-
dc.contributor.authorHuang, Jianhua Z.-
dc.date.accessioned2015-09-23T02:57:43Z-
dc.date.available2015-09-23T02:57:43Z-
dc.date.issued2012-
dc.identifier.citationAnnals of Applied Statistics, 2012, v. 6, n. 3, p. 870-893-
dc.identifier.issn1932-6157-
dc.identifier.urihttp://hdl.handle.net/10722/219682-
dc.description.abstractAccurate forecasting of zero coupon bond yields for a continuum of maturities is paramount to bond portfolio management and derivative security pricing. Yet a universal model for yield curve forecasting has been elusive, and prior attempts often resulted in a trade-off between goodness of fit and consistency with economic theory. To address this, herein we propose a novel formulation which connects the dynamic factor model (DFM) framework with concepts from functional data analysis: a DFM with functional factor loading curves. This results in a model capable of forecasting functional time series. Further, in the yield curve context we show that the model retains economic interpretation. Model estimation is achieved through an expectation- maximization algorithm, where the time series parameters and factor loading curves are simultaneously estimated in a single step. Efficient computing is implemented and a data-driven smoothing parameter is nicely incorporated. We show that our model performs very well on forecasting actual yield data compared with existing approaches, especially in regard to profit-based assessment for an innovative trading exercise. We further illustrate the viability of our model to applications outside of yield forecasting.-
dc.languageeng-
dc.relation.ispartofAnnals of Applied Statistics-
dc.subjectFunctional data analysis-
dc.subjectExpectation maximization algorithm-
dc.subjectCross-validation-
dc.subjectRoughness penalty-
dc.subjectNatural cubic splines-
dc.titleFunctional dynamic factor models with application to yield curve forecasting-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1214/12-AOAS551-
dc.identifier.scopuseid_2-s2.0-84870027049-
dc.identifier.volume6-
dc.identifier.issue3-
dc.identifier.spage870-
dc.identifier.epage893-
dc.identifier.eissn1941-7330-

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