dc.contributor.author | Matilainen, M. | |
dc.contributor.author | Miettinen, Jari | |
dc.contributor.author | Nordhausen, K. | |
dc.contributor.author | Taskinen, Sara | |
dc.contributor.editor | Aivazian, S. | |
dc.contributor.editor | Filzmoser, P. | |
dc.contributor.editor | Kharin, Y. | |
dc.date.accessioned | 2018-02-15T08:21:11Z | |
dc.date.available | 2018-02-15T08:21:11Z | |
dc.date.issued | 2016 | |
dc.identifier.citation | Matilainen, M., Miettinen, J., Nordhausen, K., & Taskinen, S. (2016). ICA and stochastic volatility models. In S. Aivazian, P. Filzmoser, & Y. Kharin (Eds.), <i>CDAM 2016 : Proceedings of the XI International Conference on Computer Data Analysis and Modeling</i> (pp. 30-37). Belarusian State University Publishing House. | |
dc.identifier.other | CONVID_26201646 | |
dc.identifier.other | TUTKAID_71106 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/57074 | |
dc.description.abstract | We consider multivariate time series where each component series is an unknown
linear combination of latent mutually independent stationary time series.
Multivariate financial time series have often periods of low volatility followed by
periods of high volatility. This kind of time series have typically non-Gaussian
stationary distributions, and therefore standard independent component analysis
(ICA) tools such as fastICA can be used to extract independent component series
even though they do not utilize any information on temporal dependence. In this
paper we review some ICA methods used in the context of stochastic volatility
models. We also suggest their modifications which use nonlinear autocorrelations
to extract independent components. Different estimates are then compared in a
simulation study | |
dc.language.iso | eng | |
dc.publisher | Belarusian State University Publishing House | |
dc.relation.ispartof | CDAM 2016 : Proceedings of the XI International Conference on Computer Data Analysis and Modeling | |
dc.relation.uri | 978-985-553-366-6 | |
dc.subject.other | blind source separation | |
dc.subject.other | GARCH model | |
dc.subject.other | nonlinear autocorrelation | |
dc.subject.other | multivariate time series | |
dc.title | ICA and stochastic volatility models | |
dc.type | conferenceObject | |
dc.identifier.urn | URN:NBN:fi:jyu-201610114322 | |
dc.contributor.laitos | Matematiikan ja tilastotieteen laitos | fi |
dc.contributor.laitos | Department of Mathematics and Statistics | en |
dc.contributor.oppiaine | Tilastotiede | fi |
dc.contributor.oppiaine | Statistics | en |
dc.type.uri | http://purl.org/eprint/type/ConferencePaper | |
dc.date.updated | 2016-10-11T06:15:08Z | |
dc.relation.isbn | 978-985-553-366-6 | |
dc.type.coar | conference paper | |
dc.description.reviewstatus | peerReviewed | |
dc.format.pagerange | 30-37 | |
dc.type.version | acceptedVersion | |
dc.rights.copyright | © the Authors & Belarusian State University Publishing House, 2016. | |
dc.rights.accesslevel | openAccess | fi |
dc.relation.conference | Computer Data Analysis and Modeling | |