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Dynamic correlations at different time-scales with empirical mode decomposition

Nava, Noemi, Di Matteo, T. and Aste, Tomaso (2018) Dynamic correlations at different time-scales with empirical mode decomposition. Physica A: Statistical Mechanics and Its Applications, 502. pp. 534-544. ISSN 0378-4371

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Identification Number: 10.1016/j.physa.2018.02.108


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.

Item Type: Article
Official URL:
Additional Information: © 2018 Elsevier B.V.
Divisions: Systemic Risk Centre
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics
Date Deposited: 20 Apr 2018 14:26
Last Modified: 20 Oct 2021 00:38
Funders: Conacyt-Mexico

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