Scagliarini Tomas, Faes Luca, Marinazzo Daniele, Stramaglia Sebastiano, Mantegna Rosario N
Dipartimento Interateneo di Fisica, Universitá Degli Studi di Bari Aldo Moro, 70126 Bari, Italy.
INFN, Sezione di Bari, 70126 Bari, Italy.
Entropy (Basel). 2020 Sep 8;22(9):1000. doi: 10.3390/e22091000.
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.
揭示股票市场指数之间的动态信息流一直是多项研究的主题，这些研究利用了转移熵或格兰杰因果关系（其线性版本）的概念。转移熵方法的输出是一个有向加权图，用于衡量每个驱动股票市场指数的状态信息所提供的关于每个目标未来状态的信息。为了超越信息流的成对描述，从而研究更高阶的信息回路，我们在此将部分信息分解应用于由一对驱动市场（属于美洲或欧洲）和一个亚洲目标市场组成的三元组。我们对2000年至2019年期间记录的日数据进行分析，从而能够识别一对驱动因素所携带的关于目标的协同信息。通过研究驱动因素的收盘价回报对目标指数随后隔夜变化的影响，我们发现：（i）韩国、东京、香港和新加坡依次是受影响最大的亚洲市场；（ii）就双变量格兰杰因果关系而言，美国指数标准普尔500和罗素是最强的驱动因素；（iii）关于高阶效应，欧美股票市场指数对作为最具协同性的三变量回路发挥着主要作用。我们的结果表明，协同性作为源于信息理论的高阶预测信息流的代理，提供了与从双变量和全局格兰杰因果关系中获得的细节互补的信息，因此可用于更好地刻画全球金融体系。
