A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Extracting conditionally heteroskedastic components using independent component analysis (2020)


Miettinen, Jari; Matilainen, Markus; Nordhausen, Klaus; Taskinen, Sara (2020). Extracting conditionally heteroskedastic components using independent component analysis. Journal of Time Series Analysis, 41 (2), 293-311. DOI: 10.1111/jtsa.12505


JYU-tekijät tai -toimittajat


Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Miettinen, Jari; Matilainen, Markus; Nordhausen, Klaus; Taskinen, Sara

Lehti tai sarja: Journal of Time Series Analysis

ISSN: 0143-9782

eISSN: 1467-9892

Julkaisuvuosi: 2020

Volyymi: 41

Lehden numero: 2

Artikkelin sivunumerot: 293-311

Kustantaja: Wiley-Blackwell

Julkaisumaa: Britannia

Julkaisun kieli: englanti

DOI: http://doi.org/10.1111/jtsa.12505

Linkki tutkimusaineistoon: https://cran.r-project.org/package=tsBSS

Avoin saatavuus: Hybridijulkaisukanavassa ilmestynyt avoin julkaisu

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/67432

Rinnakkaistallenteen verkko-osoite (pre-print): https://arxiv.org/abs/1811.10963


Tiivistelmä

In 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.


YSO-asiasanat: aikasarja-analyysi; tilastolliset mallit; GARCH-mallit; monimuuttujamenetelmät

Vapaat asiasanat: ARMA-GARCH process; asymptotic normality; autocorrelation; blind source separation; principal volatility component


Liittyvät organisaatiot


OKM-raportointi: Kyllä

Raportointivuosi: 2020

Alustava JUFO-taso: 2


Viimeisin päivitys 2020-18-08 klo 13:05