A1 Journal article (refereed)
Extracting conditionally heteroskedastic components using independent component analysis (2020)

Miettinen, J., Matilainen, M., Nordhausen, K., & Taskinen, S. (2020). Extracting conditionally heteroskedastic components using independent component analysis. Journal of Time Series Analysis, 41(2), 293-311. https://doi.org/10.1111/jtsa.12505

JYU authors or editors

Publication details

All authors or editors: Miettinen, Jari; Matilainen, Markus; Nordhausen, Klaus; Taskinen, Sara

Journal or series: Journal of Time Series Analysis

ISSN: 0143-9782

eISSN: 1467-9892

Publication year: 2020

Volume: 41

Issue number: 2

Pages range: 293-311

Publisher: Wiley-Blackwell

Publication country: United Kingdom

Publication language: English

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

Research data link: https://cran.r-project.org/package=tsBSS

Publication open access: Openly available

Publication channel open access: Partially open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/67432

Web address of parallel published publication (pre-print): https://arxiv.org/abs/1811.10963

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.

Keywords: time-series analysis; statistical models; GARCH models; multivariable methods

Free keywords: ARMA-GARCH process; asymptotic normality; autocorrelation; blind source separation; principal volatility component

Contributing organizations

Ministry reporting: Yes

VIRTA submission year: 2020

JUFO rating: 2