Blind source separation with nonlinear autocorrelation and non-Gaussianity

Shi, Zhenwei; Jiang, Zhiguo; Zhou, Fugen; Yin, Jingwei

doi:10.1016/j.cam.2008.10.031

Cited by 17 publications

(17 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The FixNA (Fixed-point algorithm for maximizing the nonlinear autocorrelation) method was introduced in Shi, Jiang, and Zhou (2009), and its criterion function to be maximized is…”

Section: Source Separation For Multivariate Time Seriesmentioning

confidence: 99%

On Independent Component Analysis with Stochastic Volatility Models

Matilainen

Miettinen

Nordhausen

et al. 2017

AJS

View full text Add to dashboard Cite

Consider a multivariate time series where each component series is assumed to be a linear mixture of latent mutually independent stationary time series. Classical independent component analysis (ICA) tools, such as fastICA, are often used to extract latent series, but they don't utilize any information on temporal dependence. Also financial time series often have periods of low and high volatility. In such settings second order source separation methods, such as SOBI, fail. We review here some classical methods used for time series with stochastic volatility, and suggest modifications of them by proposing a family of vSOBI estimators. These estimators use different nonlinearity functions to capture nonlinear autocorrelation of the time series and extract the independent components. Simulation study shows that the proposed method outperforms the existing methods when latent components follow GARCH and SV models. This paper is an invited extended version of the paper presented at the CDAM 2016 conference.

show abstract

“…The FixNA (Fixed-point algorithm for maximizing the nonlinear autocorrelation) method was introduced in Shi, Jiang, and Zhou (2009), and its criterion function to be maximized is…”

Section: Source Separation For Multivariate Time Seriesmentioning

confidence: 99%

On Independent Component Analysis with Stochastic Volatility Models

Matilainen

Miettinen

Nordhausen

et al. 2017

AJS

View full text Add to dashboard Cite

show abstract

“…In particular, the method works with the functions in a reproducing kernel Hilbert space, and make use of the kernel trick to search over this space efficiently. We also compare our method with FixNA [76], method for blind source separation problem.…”

Section: Experiments and Analysismentioning

confidence: 99%

ICA based on asymmetry

Spurek

Tabor

Rola

et al. 2017

Pattern Recognition

View full text Add to dashboard Cite

“…SOBI performs well when the independent components have non‐zero linear autocorrelations, but it fails to utilize volatility clustering information. On the other hand, ICA methods which are tailored for separating time series with volatility clustering, see for example Hyvärinen (), Shi et al (), Matilainen et al (), Matilainen et al (), do not utilize information coming from linear autocorrelations to their full extent.…”

Section: Introductionmentioning

confidence: 99%

Extracting Conditionally Heteroskedastic Components using Independent Component Analysis

Miettinen

Matilainen

Nordhausen³

et al. 2019

Journal Time Series Analysis

View full text Add to dashboard Cite

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.

show abstract

Blind source separation with nonlinear autocorrelation and non-Gaussianity

Cited by 17 publications

References 27 publications

On Independent Component Analysis with Stochastic Volatility Models

On Independent Component Analysis with Stochastic Volatility Models

ICA based on asymmetry

Extracting Conditionally Heteroskedastic Components using Independent Component Analysis

Contact Info

Product

Resources

About