英文标题：
《Time-dependent scaling patterns in high frequency financial data》
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作者：
Noemi Nava, Tiziana Di Matteo and Tomaso Aste
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最新提交年份：
2015
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英文摘要：
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.
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中文摘要：
我们测量了不同时间尺度对金融市场数据动态的影响。这是通过将金融时间序列分解为与不同时间尺度相关的简单振荡来实现的。我们提出了两种新的时变测度：1）振幅标度指数和2）类熵测度。我们将这些措施应用于各种股票指数的日内30秒抽样价格。我们的结果揭示了日内趋势，即在交易日的过程中，不同的时间范围有不同的相对振幅。我们的研究结果表明，我们分析的时间序列具有非平稳多重分形性质，在交易时段中间主要表现为持续性行为，在开盘和收盘时表现为反持续性行为。我们证明了这些偏差在统计学上是显著且稳健的。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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