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Dynamic correlations at different time-scales with empirical mode decomposition

Research output: Contribution to journalArticle

Noemi Nava, T. Di Matteo, Tomaso Aste

Original languageEnglish
StateE-pub ahead of print - 3 Mar 2018

King's Authors


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 led-lag relations that could have practical use for portfolio management, risk estimation and investment decisions.

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