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摘要翻译：
我们利用最小生成树和层次树的概念，研究了2007-2008年经济大动荡期间34种主要货币之间的关联网络拓扑结构。我们使用美元（美国元）和土耳其里拉（土耳其里拉）作为数字，其中美元是主要货币，土耳其里拉是次要货币。我们导出了一个层次结构，并构建了2007年、2008年以及2007-2008年全年的最小生成树和层次树。通过使用数据的引导副本，我们实现了一种将可靠性值与MSTs和HTs链路相关联的技术。在以TL为数值的情况下，我们还使用了平均连锁聚类分析来获得层次树。这些树是理解和检测金融数据中的全局结构、分类法和层次结构的有用工具。我们说明了最小生成树及其相关的层次树是如何在一段时间内发展起来的。从这些树中，我们根据它们的邻近程度和经济联系确定了不同的货币集群。得到了货币的聚类结构和每个聚类中的关键货币，发现该聚类与亚洲、欧洲等世界上相应国家的地理区域匹配良好。正如预期的那样，关键货币通常是那些显示主要经济活动的货币。
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英文标题：
《Topology of the correlation networks among major currencies using
hierarchical structure methods》
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作者：
Mustafa Keskin, Bayram Deviren and Yusuf Kocakaplan
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最新提交年份：
2010
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分类信息：
一级分类：Quantitative Finance 数量金融学
二级分类：General Finance 一般财务
分类描述：Development of general quantitative methodologies with applications in finance
通用定量方法的发展及其在金融中的应用
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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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英文摘要：
We studied the topology of correlation networks among 34 major currencies using the concept of a minimal spanning tree and hierarchical tree for the full years of 2007-2008 when major economic turbulence occurred. We used the USD (US Dollar) and the TL (Turkish Lira) as numeraires in which the USD was the major currency and the TL was the minor currency. We derived a hierarchical organization and constructed minimal spanning trees (MSTs) and hierarchical trees (HTs) for the full years of 2007, 2008 and for the 2007-2008 periods. We performed a technique to associate a value of reliability to the links of MSTs and HTs by using bootstrap replicas of data. We also used the average linkage cluster analysis for obtaining the hierarchical trees in the case of the TL as the numeraire. These trees are useful tools for understanding and detecting the global structure, taxonomy and hierarchy in financial data. We illustrated how the minimal spanning trees and their related hierarchical trees developed over a period of time. From these trees we identified different clusters of currencies according to their proximity and economic ties. The clustered structure of the currencies and the key currency in each cluster were obtained and we found that the clusters matched nicely with the geographical regions of corresponding countries in the world such as Asia or Europe. As expected the key currencies were generally those showing major economic activity.
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PDF链接：
https://arxiv.org/pdf/1010.5653
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