Extracting Conditionally Heteroskedastic Components using Independent Component Analysis
Loading...
Access rights
openAccess
URL
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
Date
2020-03-01
Major/Subject
Mcode
Degree programme
Language
en
Pages
293-311
Series
Journal of Time Series Analysis, Volume 41, issue 2
Abstract
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.Description
Keywords
ARMA-GARCH process, asymptotic normality, autocorrelation, blind source separation, principal volatility component
Other note
Citation
Miettinen, J, Matilainen, M, Nordhausen, K & Taskinen, S 2020, ' Extracting Conditionally Heteroskedastic Components using Independent Component Analysis ', Journal of Time Series Analysis, vol. 41, no. 2, pp. 293-311 . https://doi.org/10.1111/jtsa.12505