Time-dependent scaling patterns in high frequency financial data

Noemi Nava, Tiziana Di Matteo and Tomaso Aste

Papers from arXiv.org

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.

Date: 2015-08, Revised 2015-12
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-fmk and nep-mst
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1508.07428

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