Time-dependent scaling patterns in high frequency financial data
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.
Cite
@article{arxiv.1508.07428,
title = {Time-dependent scaling patterns in high frequency financial data},
author = {Noemi Nava and Tiziana Di Matteo and Tomaso Aste},
journal= {arXiv preprint arXiv:1508.07428},
year = {2016}
}
Comments
28 pages, 10 figures