英文标题：
《Assessment of 48 Stock markets using adaptive multifractal approach》
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作者：
Paulo Ferreira, Andreia Dion\\\'isio and S.M.S. Movahed
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最新提交年份：
2017
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英文摘要：
  Stock market comovements are examined using cointegration, Granger causality tests and nonlinear approaches in context of mutual information and correlations. Underlying data sets are affected by non-stationarities and trends, we also apply AMF-DFA and AMF-DXA. We find only 170 pair of Stock markets cointegrated, and according to the Granger causality and mutual information, we realize that the strongest relations lies between emerging markets, and between emerging and frontier markets. According to scaling exponent given by AMF-DFA, $h(q=2)>1$, we find that all underlying data sets belong to non-stationary process. According to EMH, only 8 markets are classified in uncorrelated processes at $2\\sigma$ confidence interval. 6 Stock markets belong to anti-correlated class and dominant part of markets has memory in corresponding daily index prices during January 1995 to February 2014. New-Zealand with $H=0.457\\pm0.004$ and Jordan with $H=0.602\\pm 0.006$ are far from EMH. The nature of cross-correlation exponents based on AMF-DXA is almost multifractal for all pair of Stock markets. The empirical relation, $H_{xy}\\le [H_{xx}+H_{yy}]/2$, is confirmed. Mentioned relation for $q>0$ is also satisfied while for $q<0$ there is a deviation from this relation confirming behavior of markets for small fluctuations is affected by contribution of major pair. For larger fluctuations, the cross-correlation contains information from both local and global conditions. Width of singularity spectrum for auto-correlation and cross-correlation are $\\Delta \\alpha_{xx}\\in [0.304,0.905]$ and $\\Delta \\alpha_{xy}\\in [0.246,1.178]$, respectively. The wide range of singularity spectrum for cross-correlation confirms that the bilateral relation between Stock markets is more complex. The value of $\\sigma_{DCCA}$ indicates that all pairs of stock market studied in this time interval belong to cross-correlated processes.
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中文摘要：
在相互信息和相关性的背景下，利用协整、格兰杰因果关系检验和非线性方法对股票市场的协动进行了检验。基础数据集受非平稳性和趋势的影响，我们还应用了AMF-DFA和AMF-DXA。我们发现只有170对股票市场是协整的，根据格兰杰因果关系和互信息，我们发现新兴市场之间以及新兴市场和前沿市场之间的关系最强。根据AMF-DFA给出的标度指数，$h（q=2）>1$，我们发现所有底层数据集都属于非平稳过程。根据EMH的数据，只有8个市场被划分为不相关过程，置信区间为2美元。6个股票市场属于反相关类，市场的主导部分在1995年1月至2014年2月的相应日指数价格中具有记忆性。新西兰，含$H=0.457\\pm0。004美元和Jordan的$H=0.602\\pm 0.006美元与EMH相差甚远。基于AMF-DXA的互相关指数的性质对于所有股票市场对几乎都是多重分形的。证实了经验关系，$H{xy}\\le[H{xx}+H{yy}]/2$。对于$q>0$的上述关系也令人满意，而对于$q<0$的情况，则与该关系存在偏差，证实了小波动的市场行为受主要对贡献的影响。对于较大的波动，互相关包含来自局部和全局条件的信息。自相关和互相关的奇异谱宽度分别为$\\ Delta\\alpha\\u{xx}in[0.304,0.905]$和$\\ Delta\\alpha\\u{xy}in[0.246,1.178]$。交叉相关的广泛奇异谱证实了股票市场之间的双边关系更为复杂。$\\ sigma\\uu{DCCA}$的值表明，在此时间间隔内研究的所有股票市场对都属于相互关联的过程。
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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：Data Analysis, Statistics and Probability        数据分析、统计与概率
分类描述：Methods, software and hardware for physics data analysis: data processing and storage; measurement methodology; statistical and mathematical aspects such as parametrization and uncertainties.
物理数据分析的方法、软硬件：数据处理与存储；测量方法；统计和数学方面，如参数化和不确定性。
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