On Independent Component Analysis with Stochastic Volatility Models
Matilainen, M., Miettinen, J., Nordhausen, K., Oja, H., & Taskinen, S. (2017). On Independent Component Analysis with Stochastic Volatility Models. Austrian Journal of Statistics, 46(3-4), 57-66. https://doi.org/10.17713/ajs.v46i3-4.671
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Austrian Journal of StatisticsDate
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© the Authors, 2017. This is an open access article distributed under the terms of a Creative Commons License.
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.
...
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Österreichische Statistische GesellschaftISSN Search the Publication Forum
1026-597XPublication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/26968046
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Except where otherwise noted, this item's license is described as © the Authors, 2017. This is an open access article distributed under the terms of a Creative Commons License.
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