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Conditional independence graph for nonlinear time series and its application to international financial markets

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  • Gao, Wei
  • Zhao, Hongxia

Abstract

Conditional independence graphs are proposed for describing the dependence structure of multivariate nonlinear time series, which extend the graphical modeling approach based on partial correlation. The vertexes represent the components of a multivariate time series and edges denote direct dependence between corresponding series. The conditional independence relations between component series are tested efficiently and consistently using conditional mutual information statistics and a bootstrap procedure. Furthermore, a method combining information theory with surrogate data is applied to test the linearity of the conditional dependence. The efficiency of the methods is approved through simulation time series with different linear and nonlinear dependence relations. Finally, we show how the method can be applied to international financial markets to investigate the nonlinear independence structure.

Suggested Citation

  • Gao, Wei & Zhao, Hongxia, 2013. "Conditional independence graph for nonlinear time series and its application to international financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(10), pages 2460-2469.
  • Handle: RePEc:eee:phsmap:v:392:y:2013:i:10:p:2460-2469
    DOI: 10.1016/j.physa.2012.07.002
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    References listed on IDEAS

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