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arXiv · 1508.07428

Time-dependent scaling patterns in high frequency financial data

Noemi Nava·Tiziana Di Matteo·Tomaso Aste
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Abstract

We measure the influence of different time-scales on the dynamics of financial market data. This is obtained by decomposing financial time series into simple oscillations associated with distinct time-scales. We propose two new time-varying measures: 1) an amplitude scaling exponent and 2) an entropy-like measure. We apply these measures to intraday, 30-second sampled prices of various stock indices. Our results reveal intraday trends where different time-horizons contribute with variable relative amplitudes over the course of the trading day. Our findings indicate that the time series we analysed have a non-stationary multifractal nature with predominantly persistent behaviour at the middle of the trading session and anti-persistent behaviour at the open and close. We demonstrate that these deviations are statistically significant and robust.

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Noemi Nava, Tiziana Di Matteo, Tomaso Aste. 2015-08-29. Time-dependent scaling patterns in high frequency financial data. https://doi.org/10.1140/epjst%2Fe2015-50328-y

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