摘要翻译:
我们利用纽约证券交易所TAQ数据库的日内数据,分析了2007年至2010年4年期间标准普尔500成份股的统计依赖结构。利用基于Copula的方法,我们发现在边缘分布的尾部,统计相关性非常强。这种尾部相关性比二元高斯分布中的高斯分布高,这在许多相关系数的计算中是隐含的。我们将尾部依赖与市场的平均相关水平作为一个常用的量进行比较,并揭示了一个近似线性关系。
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英文标题:
《A Copula Approach on the Dynamics of Statistical Dependencies in the US
Stock Market》
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作者:
Michael C. M\"unnix, Rudi Sch\"afer
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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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英文摘要:
We analyze the statistical dependency structure of the S&P 500 constituents in the 4-year period from 2007 to 2010 using intraday data from the New York Stock Exchange's TAQ database. With a copula-based approach, we find that the statistical dependencies are very strong in the tails of the marginal distributions. This tail dependence is higher than in a bivariate Gaussian distribution, which is implied in the calculation of many correlation coefficients. We compare the tail dependence to the market's average correlation level as a commonly used quantity and disclose an nearly linear relation.
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PDF链接:
https://arxiv.org/pdf/1102.1099