摘要翻译：
我们通过建立一个简单的模型来研究价格的随机游动，该模型将单个价格变化的符号和绝对值的性质与较长时间尺度下价格的扩散率（波动率）联系起来。我们证明了这种基准模型不能再现实际价格的扩散性质。具体来说，我们发现在一个小时的区间内，该模型对实际价格序列的波动率的预测持续超过了70%左右，并且随着区间长度的增加，这种预测效果变得更强。通过有选择地洗牌数据的某些部分，同时保留其他部分，我们能够表明，这种差异是由单个回报的迹象和规模之间微妙但长期的非同期相关性造成的。我们推测，这与交易迹象的长记忆性和强制执行市场效率的需要有关。
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英文标题：
《The non-random walk of stock prices: The long-term correlation between
  signs and sizes》
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作者：
Gabriele La Spada, J. Doyne Farmer and Fabrizio Lillo
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最新提交年份：
2008
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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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一级分类：Physics        物理学
二级分类：Physics and Society        物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
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英文摘要：
  We investigate the random walk of prices by developing a simple model relating the properties of the signs and absolute values of individual price changes to the diffusion rate (volatility) of prices at longer time scales. We show that this benchmark model is unable to reproduce the diffusion properties of real prices. Specifically, we find that for one hour intervals this model consistently over-predicts the volatility of real price series by about 70%, and that this effect becomes stronger as the length of the intervals increases. By selectively shuffling some components of the data while preserving others we are able to show that this discrepancy is caused by a subtle but long-range non-contemporaneous correlation between the signs and sizes of individual returns. We conjecture that this is related to the long-memory of transaction signs and the need to enforce market efficiency.
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PDF链接：
https://arxiv.org/pdf/0711.4596