2017/11/09 by Martin Magris, Magris, Martin, Jiyeong Kim +5
Economics, Econometrics and Finance · Physics and Astronomy · #Complex Network Analysis Techniques #Complex Systems and Time Series Analysis #FOS: Economics and business #Opinion Dynamics and Social Influence #Statistical Finance (q-fin.ST) #Trading and Market Microstructure (q-fin.TR)
paper · pdf · doi:10.48550/arxiv.1711.03534
openalex publication_date 2017/11/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Long-range correlation in financial time series reflects the complex dynamics\nof the stock markets driven by algorithms and human decisions. Our analysis\nexploits ultra-high frequency order book data from NASDAQ Nordic over a period\nof three years to numerically estimate the power-law scaling exponents using\ndetrended fluctuation analysis (DFA). We address inter-event durations (order\nto order, trade to trade, cancel to cancel) as well as cross-event durations\n(time from order submission to its trade or cancel). We find strong evidence of\nlong-range correlation, which is consistent across different stocks and\nvariables. However, given the crossovers in the DFA fluctuation functions, our\nresults indicate that the long-range correlation in inter-event durations\nbecomes stronger over a longer time scale, i.e., when moving from a range of\nhours to days and further to months. We also observe interesting associations\nbetween the scaling exponent and a number of economic variables, in particular,\nin the inter-trade time series.\n