摘要翻译：
我们研究了连续交易量超过一定阈值$q$之间的递归间隔$\tau$的统计性质。本文对2000年1月至2009年5月的20只中国流通股和2003年1月至2009年4月的两只中国股指进行了回归区间分析。与价格收益的递推区间分布类似，交易量的递推区间分布的尾部服从幂律标度，并用Kolmogorov-Smirnov(KS)统计量、加权KS统计量和Crm{e}}r-von Mises准则进行了拟合优度检验。对条件概率分布和减值波动函数的度量表明，在交易量之间的重复间隔中，存在短期和长期记忆效应。基于递推区间分析方法，进一步研究了交易量与价格收益之间的关系。研究发现，较大的交易量往往伴随着较大的价格收益，交易量与价格收益之间的协同效应在交易量较大的情况下更为明显。
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英文标题：
《Recurrence interval analysis of trading volumes》
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作者：
Fei Ren and Wei-Xing Zhou
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最新提交年份：
2010
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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：Quantitative Finance        数量金融学
二级分类：Trading and Market Microstructure        交易与市场微观结构
分类描述：Market microstructure, liquidity, exchange and auction design, automated trading, agent-based modeling and market-making
市场微观结构，流动性，交易和拍卖设计，自动化交易，基于代理的建模和做市
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英文摘要：
  We study the statistical properties of the recurrence intervals $\tau$ between successive trading volumes exceeding a certain threshold $q$. The recurrence interval analysis is carried out for the 20 liquid Chinese stocks covering a period from January 2000 to May 2009, and two Chinese indices from January 2003 to April 2009. Similar to the recurrence interval distribution of the price returns, the tail of the recurrence interval distribution of the trading volumes follows a power-law scaling, and the results are verified by the goodness-of-fit tests using the Kolmogorov-Smirnov (KS) statistic, the weighted KS statistic and the Cram{\'{e}}r-von Mises criterion. The measurements of the conditional probability distribution and the detrended fluctuation function show that both short-term and long-term memory effects exist in the recurrence intervals between trading volumes. We further study the relationship between trading volumes and price returns based on the recurrence interval analysis method. It is found that large trading volumes are more likely to occur following large price returns, and the comovement between trading volumes and price returns is more pronounced for large trading volumes.
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PDF链接：
https://arxiv.org/pdf/1002.1653