摘要翻译：
本文应用RMT、网络和MF-DFA方法研究了20个全球金融指数的相关性、网络性和多重分形性。我们分别比较了2008年金融危机前和金融危机期间的结果。我们发现，与由第二大特征值对应的特征向量得到的结果相比，网络方法提供了更多关于簇形成的有用信息，这些扇区是基于指数的地理位置形成的。在临界值0.6，美洲、欧洲和亚洲/太平洋对应的指数在危机前脱节并形成不同的群组，但在危机期间，美洲和欧洲对应的指数组合在一起形成一个群组，亚洲/太平洋指数形成另一个群组。通过将阈值进一步提高到0.9，欧洲国家法国、德国和英国构成了联系最紧密的市场。我们研究了全球金融指数的多重分形性质，发现与其他金融指数相比，美洲和欧洲的金融指数几乎处于相同的多重分形度范围内。印度、韩国、香港与美洲和欧洲指数的多重分形程度接近。埃及、印度尼西亚、马来西亚、台湾和新加坡的多重分形程度差异很大，这可能是当我们提高金融网络的阈值时，这些国家首先开始在低阈值时与金融指数相关网络脱节的原因。我们将二项式多重分形模型(BMFM)应用于这些金融市场。
---
英文标题：
《Correlation, Network and Multifractal Analysis of Global Financial
  Indices》
---
作者：
Sunil Kumar and Nivedita Deo
---
最新提交年份：
2012
---
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
--
一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
--

---
英文摘要：
  We apply RMT, Network and MF-DFA methods to investigate correlation, network and multifractal properties of 20 global financial indices. We compare results before and during the financial crisis of 2008 respectively. We find that the network method gives more useful information about the formation of clusters as compared to results obtained from eigenvectors corresponding to second largest eigenvalue and these sectors are formed on the basis of geographical location of indices. At threshold 0.6, indices corresponding to Americas, Europe and Asia/Pacific disconnect and form different clusters before the crisis but during the crisis, indices corresponding to Americas and Europe are combined together to form a cluster while the Asia/Pacific indices forms another cluster. By further increasing the value of threshold to 0.9, European countries France, Germany and UK constitute the most tightly linked markets. We study multifractal properties of global financial indices and find that financial indices corresponding to Americas and Europe almost lie in the same range of degree of multifractality as compared to other indices. India, South Korea, Hong Kong are found to be near the degree of multifractality of indices corresponding to Americas and Europe. A large variation in the degree of multifractality in Egypt, Indonesia, Malaysia, Taiwan and Singapore may be a reason that when we increase the threshold in financial network these countries first start getting disconnected at low threshold from the correlation network of financial indices. We fit Binomial Multifractal Model (BMFM) to these financial markets.
---
PDF链接：
https://arxiv.org/pdf/1202.0409