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TVICA—Time varying independent component analysis and its application to financial data

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  • Chen, Ray-Bing
  • Chen, Ying
  • Härdle, Wolfgang K.

Abstract

A new method of ICA, TVICA, is proposed. Compared to the conventional ICA, the TVICA method allows the mixing matrix to be time dependent. Estimation is conducted under local homogeneity that assumes at any particular time point, there exists an interval over which the mixing matrix can be well approximated as constant. A sequential log likelihood-ratio testing procedure is used to automatically identify such local intervals. Numerical analysis demonstrates that TVICA provides good performance in homogeneous situations and does improve accuracy in nonstationary settings with possible structural change. In real data analysis with application to risk management, the TVICA confirms a superior performance when compared to several alternatives, including ICA, PCA and DCC-based models.

Suggested Citation

  • Chen, Ray-Bing & Chen, Ying & Härdle, Wolfgang K., 2014. "TVICA—Time varying independent component analysis and its application to financial data," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 95-109.
    • Handle: RePEc:eee:csdana:v:74:y:2014:i:c:p:95-109
    DOI: 10.1016/j.csda.2014.01.002
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    References listed on IDEAS

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    Cited by:

    1. Saima Afzal & Muhammad Mutahir Iqbal, 2016. "A new way to order independent components," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(9), pages 1753-1764, July.
    2. Chen, Ying & Niu, Linlin & Chen, Ray-Bing & He, Qiang, 2019. "Sparse-Group Independent Component Analysis with application to yield curves prediction," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 76-89.
    3. Tran Hoang Hai, 2020. "Estimation of volatility causality in structural autoregressions with heteroskedasticity using independent component analysis," Statistical Papers, Springer, vol. 61(1), pages 1-16, February.

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