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Dynamic correlations at different time-scales with empirical mode decomposition

Nava, N; Di Matteo, T; Aste, T; (2018) Dynamic correlations at different time-scales with empirical mode decomposition. Physica A: Statistical Mechanics and its Applications , 502 pp. 534-544. 10.1016/j.physa.2018.02.108. Green open access

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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.

Type: Article
Title: Dynamic correlations at different time-scales with empirical mode decomposition
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.physa.2018.02.108
Publisher version: https://doi.org/10.1016/j.physa.2018.02.108
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Time-scale-dependent correlation, Time-dependent correlation, Empirical mode decomposition
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1572725
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