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Regularization for stationary multivariate time series

  • Yan Sun
  • Xiaodong Lin
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    The complexity of multivariate time series models increases dramatically when the number of component series increases. This is a phenomenon observed in both low- and high-frequency financial data analysis. In this paper, we develop a regularization framework for multivariate time series models based on the penalized likelihood method. We show that, under certain conditions, the regularized estimators are sparse-consistent and satisfy an asymptotic normality. This framework provides a theoretical foundation for addressing the curse of dimensionality in multivariate econometric models. We illustrate the utility of our method by developing a sparse version of the full-factor multivariate GARCH model. We successfully apply this model to simulated data as well as the minute returns of the Dow Jones industrial average component stocks.

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    Article provided by Taylor & Francis Journals in its journal Quantitative Finance.

    Volume (Year): 12 (2012)
    Issue (Month): 4 (January)
    Pages: 573-586

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    Handle: RePEc:taf:quantf:v:12:y:2012:i:4:p:573-586
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