{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:13:43Z","timestamp":1760238823745,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,9,8]],"date-time":"2020-09-08T00:00:00Z","timestamp":1599523200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003407","name":"Ministero dell\u2019Istruzione, dell\u2019Universit\u00e0 e della Ricerca","doi-asserted-by":"publisher","award":["PRIN 2017WZFTZP \"Stochastic forecasting in complex systems\u201d"],"award-info":[{"award-number":["PRIN 2017WZFTZP \"Stochastic forecasting in complex systems\u201d"]}],"id":[{"id":"10.13039\/501100003407","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/e22091000","type":"journal-article","created":{"date-parts":[[2020,9,8]],"date-time":"2020-09-08T09:03:48Z","timestamp":1599555828000},"page":"1000","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Synergistic Information Transfer in the Global System of Financial Markets"],"prefix":"10.3390","volume":"22","author":[{"given":"Tomas","family":"Scagliarini","sequence":"first","affiliation":[{"name":"Dipartimento Interateneo di Fisica, Universit\u00e1 Degli Studi di Bari Aldo Moro, 70126 Bari, Italy"},{"name":"INFN, Sezione di Bari, 70126 Bari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3271-5348","authenticated-orcid":false,"given":"Luca","family":"Faes","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria, Universit\u00e1 di Palermo, 90128 Palermo, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9803-0122","authenticated-orcid":false,"given":"Daniele","family":"Marinazzo","sequence":"additional","affiliation":[{"name":"Data Analysis Department, Ghent University, 9000 Ghent, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5873-8564","authenticated-orcid":false,"given":"Sebastiano","family":"Stramaglia","sequence":"additional","affiliation":[{"name":"Dipartimento Interateneo di Fisica, Universit\u00e1 Degli Studi di Bari Aldo Moro, 70126 Bari, Italy"},{"name":"INFN, Sezione di Bari, 70126 Bari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4177-171X","authenticated-orcid":false,"given":"Rosario N.","family":"Mantegna","sequence":"additional","affiliation":[{"name":"Dipartimento di Fisica e Chimica, Universit\u00e1 di Palermo, 90123 Palermo, Italy"},{"name":"Complexity Science Hub Vienna, 1080 Vienna, Austria"},{"name":"Computer Science Department, University College London, London WC1E 6BT, UK"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,8]]},"reference":[{"key":"ref_1","unstructured":"Wooldridge, D.P., Domanski, D., and Cobau, A. 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