摘要翻译：
我们利用每日和日内数据集，研究了日本股票市场在一定阈值$q$以上的价格波动之间的回报间隔的尺度和记忆效应。我们发现，收益区间的分布可以用一个只依赖于收益区间$\tau$与其均值$<\tau>$之比的标度函数来近似。通过研究条件分布和平均返回间隔，我们还发现了大（或小）返回间隔跟随大（或小）返回间隔的记忆效应。结果与以往对其他市场的研究相似，并表明不同的金融市场具有相似的统计特征。我们还比较了1989年底大崩盘前后的结果。我们发现，尽管收益率的统计性质不同，但收益率区间的标度效应和记忆效应表现出相似的特征。
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英文标题：
《Volatility return intervals analysis of the Japanese market》
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作者：
Woo-Sung Jung, Fengzhong Wang, Shlomo Havlin, Taisei Kaizoji, Hie-Tae
  Moon, H. Eugene Stanley
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最新提交年份：
2007
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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：Statistical Mechanics        统计力学
分类描述：Phase transitions, thermodynamics, field theory, non-equilibrium phenomena, renormalization group and scaling, integrable models, turbulence
相变，热力学，场论，非平衡现象，重整化群和标度，可积模型，湍流
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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 scaling and memory effects in return intervals between price volatilities above a certain threshold $q$ for the Japanese stock market using daily and intraday data sets. We find that the distribution of return intervals can be approximated by a scaling function that depends only on the ratio between the return interval $\tau$ and its mean $<\tau>$. We also find memory effects such that a large (or small) return interval follows a large (or small) interval by investigating the conditional distribution and mean return interval. The results are similar to previous studies of other markets and indicate that similar statistical features appear in different financial markets. We also compare our results between the period before and after the big crash at the end of 1989. We find that scaling and memory effects of the return intervals show similar features although the statistical properties of the returns are different.
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PDF链接：
https://arxiv.org/pdf/0709.1725