Name
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.physa.2018.02.108 | Physica A: Statistical Mechanics and its Applications |
Keywords | Field | DocType |
Time-scale-dependent correlation,Time-dependent correlation,Empirical mode decomposition | Econometrics,Project portfolio management,Investment decisions,Volatility (finance),Mathematics,Hilbert–Huang transform | Journal |
Volume | ISSN | Citations |
502 | 0378-4371 | 2 |
PageRank | References | Authors |
0.40 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Noemi Nava | 1 | 2 | 0.40 |
| T. Di Matteo | 2 | 5 | 1.34 |
| Tomaso Aste | 3 | 57 | 11.62 |