Long-range correlation in financial time series reflects the complex dynamics of the stock markets driven by algorithms and human decisions. Our analysis exploits ultra-high 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.
翻译:金融时间序列的长距离关联反映了由算法和人类决定驱动的股票市场的复杂动态。我们的分析利用了来自NASDAQ National北欧的超高频订单账本数据,为期三年,利用分流波动分析(DFA)对权力法缩放指数进行数字估计。我们处理的是活动间持续时间(命令、交易、取消)和跨活动持续时间(从提交订单到交易或取消交易的时间)以及跨活动持续时间(从提交订单到交易或取消交易的时间),我们发现有强有力的证据证明长期关联,这在不同股票和变量之间是一致的。然而,鉴于DFA波动功能的交叉,我们的结果显示,在较长的时间内,即从一个小时到数天和数月之间,活动间持续时间的长距离关系会变得更大。我们还观察了缩放时间与若干经济变量之间的有趣关联,特别是在贸易时间序列中。