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TVICA - Time Varying Independent Component Analysis and Its Application to Financial Data

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  • Ray-Bing Chen
  • Ying Chen
  • Wolfgang Härdle

Abstract

Source extraction and dimensionality reduction are important in analyzing high dimensional and complex financial time series that are neither Gaussian distributed nor stationary. Independent component analysis (ICA) method can be used to factorize the data into a linear combination of independent compo- nents, so that the high dimensional problem is converted to a set of univariate ones. However conventional ICA methods implicitly assume stationarity or stochastic homogeneity of the analyzed time series, which leads to a low accu- racy of estimation in case of a changing stochastic structure. A time varying ICA (TVICA) is proposed here. The key idea is to allow the ICA filter to change over time, and to estimate it in so-called local homogeneous intervals. The question of how to identify these intervals is solved by the LCP (local change point) method. Compared to a static ICA, the dynamic TVICA pro- vides good performance both in simulation and real data analysis. The data example is concerned with independent signal processing and deals with a portfolio of highly traded stocks.

Suggested Citation

  • Ray-Bing Chen & Ying Chen & Wolfgang Härdle, 2011. "TVICA - Time Varying Independent Component Analysis and Its Application to Financial Data," SFB 649 Discussion Papers SFB649DP2011-054, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  • Handle: RePEc:hum:wpaper:sfb649dp2011-054
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    Cited by:

    1. Robinson, Peter & Taylor, Luke, 2017. "Adaptive estimation in multiple time series with independent component errors," LSE Research Online Documents on Economics 68345, London School of Economics and Political Science, LSE Library.
    2. repec:bla:jtsera:v:38:y:2017:i:2:p:191-203 is not listed on IDEAS

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    Keywords

    Adaptive Sequential Testing; Independent Component Analysis; Local Homogeneity; Signal Processing; Realized Volatility.;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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