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dc.contributor.authorMiettinen, Jari
dc.contributor.authorMatilainen, Markus
dc.contributor.authorNordhausen, Klaus
dc.contributor.authorTaskinen, Sara
dc.date.accessioned2020-01-21T12:57:06Z
dc.date.available2020-01-21T12:57:06Z
dc.date.issued2020
dc.identifier.citationMiettinen, J., Matilainen, M., Nordhausen, K., & Taskinen, S. (2020). Extracting conditionally heteroskedastic components using independent component analysis. <i>Journal of Time Series Analysis</i>, <i>41</i>(2), 293-311. <a href="https://doi.org/10.1111/jtsa.12505" target="_blank">https://doi.org/10.1111/jtsa.12505</a>
dc.identifier.otherCONVID_32834454
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/67432
dc.description.abstractIn the independent component model, the multivariate data are assumed to be a mixture of mutually independent latent components. The independent component analysis (ICA) then aims at estimating these latent components. In this article, we study an ICA method which combines the use of linear and quadratic autocorrelations to enable efficient estimation of various kinds of stationary time series. Statistical properties of the estimator are studied by finding its limiting distribution under general conditions, and the asymptotic variances are derived in the case of ARMA‐GARCH model. We use the asymptotic results and a finite sample simulation study to compare different choices of a weight coefficient. As it is often of interest to identify all those components which exhibit stochastic volatility features we suggest a test statistic for this problem. We also show that a slightly modified version of the principal volatility component analysis can be seen as an ICA method. Finally, we apply the estimators in analysing a data set which consists of time series of exchange rates of seven currencies to US dollar. Supporting information including proofs of the theorems is available online.en
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.language.isoeng
dc.publisherWiley-Blackwell
dc.relation.ispartofseriesJournal of Time Series Analysis
dc.rightsCC BY 4.0
dc.subject.otherARMA-GARCH process
dc.subject.otherasymptotic normality
dc.subject.otherautocorrelation
dc.subject.otherblind source separation
dc.subject.otherprincipal volatility component
dc.titleExtracting conditionally heteroskedastic components using independent component analysis
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202001211385
dc.contributor.laitosMatematiikan ja tilastotieteen laitosfi
dc.contributor.laitosDepartment of Mathematics and Statisticsen
dc.contributor.oppiaineTilastotiedefi
dc.contributor.oppiaineStatisticsen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.description.reviewstatuspeerReviewed
dc.format.pagerange293-311
dc.relation.issn0143-9782
dc.relation.numberinseries2
dc.relation.volume41
dc.type.versionpublishedVersion
dc.rights.copyright© 2019 the Author(s)
dc.rights.accesslevelopenAccessfi
dc.subject.ysoGARCH-mallit
dc.subject.ysomonimuuttujamenetelmät
dc.subject.ysotilastolliset mallit
dc.subject.ysoaikasarja-analyysi
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p38162
jyx.subject.urihttp://www.yso.fi/onto/yso/p2131
jyx.subject.urihttp://www.yso.fi/onto/yso/p26278
jyx.subject.urihttp://www.yso.fi/onto/yso/p22747
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.datasethttps://cran.r-project.org/package=tsBSS
dc.relation.doi10.1111/jtsa.12505
jyx.fundinginformationThe work of K.N. was supported by the Austrian Science Fund (FWF) Grant number P31881‐N32.


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