Synergistic Information Transfer in the Global System of Financial Markets.
Synergistic Information Transfer in the Global System of Financial Markets.
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DOI：
10.3390/e22091000
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发表时间：
2020-09-08
期刊：
影响因子：
--
通讯作者：
Mantegna RN
中科院分区：
文献类型：
--
作者：
Scagliarini T;Faes L;Marinazzo D;Stramaglia S;Mantegna RN
揭示股票市场指数之间的动态信息流一直是几项研究的主题，这些研究利用了传递熵或格兰杰因果关系（其线性版本）的概念。传递熵方法的输出是测量由每个驱动股票市场指数的状态的知识提供的关于每个目标的未来状态的信息的有向加权图。为了超越对信息流的成对描述，从而着眼于更高阶的信息回路，在这里，我们将部分信息分解应用于由一对驱动市场（属于美国或欧洲）和亚洲的目标市场组成的三元组。我们对2000年至2019年期间记录的日常数据进行分析，可以识别一对驱动程序携带的目标协同信息。通过研究驱动因素对目标指数隔夜变化的影响，我们发现：（i）韩国、东京、香港和新加坡依次是亚洲市场受影响最大的市场;（ii）美国指数SP 500和罗素是双变量格兰杰因果关系最强的驱动因素;（iii）在高阶效应方面，欧美股市指数对作为最具协同效应的三变量回路发挥了主要作用。我们的研究结果表明，协同作用，一个代理的高阶预测信息流植根于信息理论，提供的细节是互补的，从双变量和全球格兰杰因果关系，从而可以用来得到一个更好的表征全球金融体系。
Uncovering dynamic information flow between stock market indices has been the topic of several studies which exploited the notion of transfer entropy or Granger causality, its linear version. The output of the transfer entropy approach is a directed weighted graph measuring the information about the future state of each target provided by the knowledge of the state of each driving stock market index. In order to go beyond the pairwise description of the information flow, thus looking at higher order informational circuits, here we apply the partial information decomposition to triplets consisting of a pair of driving markets (belonging to America or Europe) and a target market in Asia. Our analysis, on daily data recorded during the years 2000 to 2019, allows the identification of the synergistic information that a pair of drivers carry about the target. By studying the influence of the closing returns of drivers on the subsequent overnight changes of target indexes, we find that (i) Korea, Tokyo, Hong Kong, and Singapore are, in order, the most influenced Asian markets; (ii) US indices SP500 and Russell are the strongest drivers with respect to the bivariate Granger causality; and (iii) concerning higher order effects, pairs of European and American stock market indices play a major role as the most synergetic three-variables circuits. Our results show that the Synergy, a proxy of higher order predictive information flow rooted in information theory, provides details that are complementary to those obtained from bivariate and global Granger causality, and can thus be used to get a better characterization of the global financial system.
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影响因子：
2.7
作者：
Faes, Luca;Marinazzo, Daniele;Stramaglia, Sebastiano
通讯作者：
Stramaglia, Sebastiano
DOI：
10.1016/j.physa.2009.04.028
发表时间：
2009-09-01
影响因子：
3.3
作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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DOI：
10.1016/j.physa.2011.12.064
发表时间：
2012-05-15
影响因子：
3.3
作者：
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通讯作者：
Shefaat, Mohammad
影响因子：
2.6
作者：
Bossomaier, Terry;Barnett, Lionel;Duncan, Rod
通讯作者：
Duncan, Rod