A1 Journal article – refereed
On Independent Component Analysis with Stochastic Volatility Models




List of Authors: Markus Matilainen, Jari Miettinen, Klaus Nordhausen, Hannu Oja, Sara Taskinen
Publisher: Österreichische Statistische Gesellschaft
Publication year: 2017
Journal: Austrian Journal of Statistics
Volume number: 46
Issue number: 3-4
Number of pages: 10

Abstract

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 nancial 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 modi cations of them by proposing a family of vSOBI estimators. These estimators use dierent 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.


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Last updated on 2019-20-07 at 08:05