摘要翻译：
对金融时间序列中的程式化事实进行了分析。我们发现，绝对收益自相关函数中的慢衰减行为实际上与金融时间序列中大波动的聚类程度直接相关，而不是资产收益分布中的重尾。我们还引入了一个指数来定量地度量这些时间序列中波动的聚类行为，并表明金融市场中的大损失通常比大收益更严重。我们进一步举例说明，与传统方法相比，我们的指数能够从金融时间序列中提取更多的信息。
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英文标题：
《Asset returns and volatility clustering in financial time series》
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作者：
Jie-Jun Tseng and Sai-Ping Li
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最新提交年份：
2011
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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：
  An analysis of the stylized facts in financial time series is carried out. We find that, instead of the heavy tails in asset return distributions, the slow decay behaviour in autocorrelation functions of absolute returns is actually directly related to the degree of clustering of large fluctuations within the financial time series. We also introduce an index to quantitatively measure the clustering behaviour of fluctuations in these time series and show that big losses in financial markets usually lump more severely than big gains. We further give examples to demonstrate that comparing to conventional methods, our index enables one to extract more information from the financial time series.
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PDF链接：
https://arxiv.org/pdf/1002.0284