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摘要翻译：
我们使用多层网络证明，在全球几个国家中，从市场指数构建的金融网络的动态与从贸易、外国直接投资等宏观经济变量构建的宏观经济网络之间存在经验联系。金融变量和宏观经济基本面动态的时间尺度是非常不同的，这使得经验联系更加有趣和重要。此外，我们发现，在各自的网络中，存在着由类似的一组国家组成的核心-外围结构（通过中心度衡量确定）----这一结果可能与国家一级宏观经济网络的“引力模型”有关。因此，从多边开放的角度，我们阐明了对单个国家而言，更大的贸易连通性与更高的金融收益相关性正相关。此外，我们还发现经济复杂度指数与股票市场之间存在正相关关系，国内生产总值也是如此。使用网络理论的数据科学方法，加上标准的计量经济学技术，构成了一种综合研究多层次经济现象的新方法。
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英文标题：
《Multi-layered Network Structure: Relationship Between Financial and
Macroeconomic Dynamics》
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作者：
Kiran Sharma, Anindya S. Chakrabarti and Anirban Chakraborti
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最新提交年份：
2019
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分类信息：
一级分类：Economics 经济学
二级分类：General Economics 一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Quantitative Finance 数量金融学
二级分类：Economics 经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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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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英文摘要：
We demonstrate using multi-layered networks, the existence of an empirical linkage between the dynamics of the financial network constructed from the market indices and the macroeconomic networks constructed from macroeconomic variables such as trade, foreign direct investments, etc. for several countries across the globe. The temporal scales of the dynamics of the financial variables and the macroeconomic fundamentals are very different, which make the empirical linkage even more interesting and significant. Also, we find that there exist in the respective networks, core-periphery structures (determined through centrality measures) that are composed of the similar set of countries -- a result that may be related through the `gravity model' of the country-level macroeconomic networks. Thus, from a multi-lateral openness perspective, we elucidate that for individual countries, larger trade connectivity is positively associated with higher financial return correlations. Furthermore, we show that the Economic Complexity Index and the equity markets have a positive relationship among themselves, as is the case for Gross Domestic Product. The data science methodology using network theory, coupled with standard econometric techniques constitute a new approach to studying multi-level economic phenomena in a comprehensive manner.
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PDF链接：
https://arxiv.org/pdf/1805.06829
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