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Dynamic correlations at different time-scales with empirical mode decomposition

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  • Nava, Noemi
  • Di Matteo, T.
  • Aste, Tomaso

Abstract

We introduce a simple approach which combines Empirical Mode Decomposition (EMD) and Pearson’s cross-correlations over rolling windows to quantify dynamic dependency at different time scales. The EMD is a tool to separate time series into implicit components which oscillate at different time-scales. We apply this decomposition to intraday time series of the following three financial indices: the S&P 500 (USA), the IPC (Mexico) and the VIX (volatility index USA), obtaining time-varying multidimensional cross-correlations at different time-scales. The correlations computed over a rolling window are compared across the three indices, across the components at different time-scales and across different time lags. We uncover a rich heterogeneity of interactions, which depends on the time-scale and has important lead–lag relations that could have practical use for portfolio management, risk estimation and investment decisions.

Suggested Citation

  • Nava, Noemi & Di Matteo, T. & Aste, Tomaso, 2018. "Dynamic correlations at different time-scales with empirical mode decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 534-544.
    • Handle: RePEc:eee:phsmap:v:502:y:2018:i:c:p:534-544
    DOI: 10.1016/j.physa.2018.02.108
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    References listed on IDEAS

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