Long-range correlation in financial time series reflects the complex dynamics of the stock markets driven by algorithms and human decisions. Our analysis exploits ultrahigh frequency order book data from NASDAQ Nordic over a period of three years to numerically estimate the power-law scaling exponents using detrended fluctuation analysis (DFA). We address inter-event durations (order to order, trade to trade, cancel to cancel) as well as cross-event durations (time from order submission to its trade or cancel). We find strong evidence of long-range correlation, which is consistent across different stocks and variables. However, given the crossovers in the DFA fluctuation functions, our results indicate that the long-range correlation in inter-event durations becomes stronger over a longer time scale, i.e., when moving from a range of hours to days and further to months. We also observe interesting associations between the scaling exponent and a number of economic variables, in particular, in the inter-trade time series.
|Title of host publication
|2017 IEEE Symposium Series on Computational Intelligence (SSCI)
|Subtitle of host publication
|November 27 - December 1 2017, Hawaii, USA.
|Number of pages
|Published - 2017
|A4 Article in conference proceedings
|IEEE Symposium Series on Computational Intelligence (IEEE SSCI) - Honolulu
Duration: 27 Nov 2017 → 1 Dec 2017
|IEEE Symposium Series on Computational Intelligence (IEEE SSCI)
|27/11/17 → 1/12/17
- DETRENDED FLUCTUATION ANALYSIS
- HURST EXPONENT
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