Nava, N;
Di Matteo, T;
Aste, T;
(2016)
Anomalous volatility scaling in high frequency financial data.
Physica A: Statistical Mechanics and its Applications
, 447
pp. 434-445.
10.1016/j.physa.2015.12.022.
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Abstract
Volatility of intra-day stock market indices computed at various time horizons exhibits a scaling behaviour that differs from what would be expected from fractional Brownian motion (fBm). We investigate this anomalous scaling by using empirical mode decomposition (EMD), a method which separates time series into a set of cyclical components at different time-scales. By applying the EMD to fBm, we retrieve a scaling law that relates the variance of the components to a power law of the oscillating period. In contrast, when analysing 22 different stock market indices, we observe deviations from the fBm and Brownian motion scaling behaviour. We discuss and quantify these deviations, associating them to the characteristics of financial markets, with larger deviations corresponding to less developed markets.
Type: | Article |
---|---|
Title: | Anomalous volatility scaling in high frequency financial data |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1016/j.physa.2015.12.022 |
Publisher version: | http://dx.doi.org/10.1016/j.physa.2015.12.022 |
Language: | English |
Additional information: | Copyright © 2015 Elsevier B.V. All rights reserved. This is a submitted version; the final published article is available at http://www.sciencedirect.com/science/article/pii/S0378437115010493 |
Keywords: | Empirical mode decomposition, Hurst exponent, Multi-scaling, Market efficiency |
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/1480594 |
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