英文标题：
《Dependence structure of market states》
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作者：
Desislava Chetalova, Marcel Wollschl\\\"ager and Rudi Sch\\\"afer
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最新提交年份：
2015
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英文摘要：
We study the dependence structure of market states by estimating empirical pairwise copulas of daily stock returns. We consider both original returns, which exhibit time-varying trends and volatilities, as well as locally normalized ones, where the non-stationarity has been removed. The empirical pairwise copula for each state is compared with a bivariate K-copula. This copula arises from a recently introduced random matrix model, in which non-stationary correlations between returns are modeled by an ensemble of random matrices. The comparison reveals overall good agreement between empirical and analytical copulas, especially for locally normalized returns. Still, there are some deviations in the tails. Furthermore, we find an asymmetry in the dependence structure of market states. The empirical pairwise copulas exhibit a stronger lower tail dependence, particularly in times of crisis.
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中文摘要：
我们通过估计每日股票收益率的经验成对copula来研究市场状态的依赖结构。我们考虑了原始收益率（表现出时变趋势和波动性）和局部标准化收益率（去除了非平稳性）。将每个状态的经验成对copula与二元K-copula进行比较。这个copula模型源于最近引入的随机矩阵模型，其中收益之间的非平稳相关性由一组随机矩阵建模。比较表明，经验连接函数和分析连接函数总体上是一致的，特别是对于局部标准化收益。尽管如此，尾部还是存在一些偏差。此外，我们发现市场国家的依赖结构是不对称的。经验两两连接函数表现出更强、更低的尾部依赖性，尤其是在危机时期。
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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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