英文标题：
《Anomalous volatility scaling in high frequency financial data》
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作者：
Noemi Nava, T. Di Matteo, Tomaso Aste
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最新提交年份：
2015
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英文摘要：
Volatility of intra-day stock market indices computed at various time horizons exhibits a scaling behaviour that differs from what would be expected from fractional Brownian motion (fBm). We investigate this anomalous scaling by using empirical mode decomposition (EMD), a method which separates time series into a set of cyclical components at different time-scales. By applying the EMD to fBm, we retrieve a scaling law that relates the variance of the components to a power law of the oscillating period. In contrast, when analysing 22 different stock market indices, we observe deviations from the fBm and Brownian motion scaling behaviour. We discuss and quantify these deviations, associating them to the characteristics of financial markets, with larger deviations corresponding to less developed markets.
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中文摘要：
在不同时间范围内计算的日内股票市场指数的波动性表现出一种标度行为，与分数布朗运动（fBm）的预期不同。我们使用经验模式分解（EMD）来研究这种反常的标度，EMD是一种将时间序列划分为一组不同时间标度的周期性分量的方法。通过将EMD应用于fBm，我们获得了一个标度律，该标度律将各分量的方差与振荡周期的幂律联系起来。相比之下，在分析22种不同的股票市场指数时，我们观察到了与fBm和布朗运动标度行为的偏差。我们讨论并量化这些偏差，将其与金融市场的特征联系起来，与欠发达市场相对应的较大偏差相关联。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Computational Finance 计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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