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摘要翻译：
通过对时间序列进行置乱，我们证明了当时间相关性结构被破坏时，股票指数的收益/损失不对称性将消失。我们还表明，由一些个股的简单平均构成的人工指数显示出收益/损失的不对称性--这允许我们明确地分析指数成分之间的依赖关系。我们考虑了基于互信息和相关性的度量，并表明股票收益在市场下跌时确实比上涨时有更高的依赖程度。
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英文标题：
《Temporal structure and gain/loss asymmetry for real and artificial stock
indices》
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作者：
Johannes Vitalis Siven, Jeffrey Todd Lins
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最新提交年份：
2009
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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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英文摘要：
We demonstrate that the gain/loss asymmetry observed for stock indices vanishes if the temporal dependence structure is destroyed by scrambling the time series. We also show that an artificial index constructed by a simple average of a number of individual stocks display gain/loss asymmetry - this allows us to explicitly analyze the dependence between the index constituents. We consider mutual information and correlation based measures and show that the stock returns indeed have a higher degree of dependence in times of market downturns than upturns.
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PDF链接：
https://arxiv.org/pdf/0907.0554
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