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Synergistic Information Transfer in the Global System of Financial Markets

Dipartimento Interateneo di Fisica, Universitá Degli Studi di Bari Aldo Moro, 70126 Bari, Italy
INFN, Sezione di Bari, 70126 Bari, Italy
Dipartimento di Ingegneria, Universitá di Palermo, 90128 Palermo, Italy
Data Analysis Department, Ghent University, 9000 Ghent, Belgium
Dipartimento di Fisica e Chimica, Universitá di Palermo, 90123 Palermo, Italy
Complexity Science Hub Vienna, 1080 Vienna, Austria
Computer Science Department, University College London, London WC1E 6BT, UK
Author to whom correspondence should be addressed.
Entropy 2020, 22(9), 1000;
Received: 15 July 2020 / Revised: 1 September 2020 / Accepted: 6 September 2020 / Published: 8 September 2020
(This article belongs to the Special Issue Applications of Information Theory in Neuroscience and Econometrics)
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. View Full-Text
Keywords: synergy; higher order dependencies; financial markets synergy; higher order dependencies; financial markets
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MDPI and ACS Style

Scagliarini, T.; Faes, L.; Marinazzo, D.; Stramaglia, S.; Mantegna, R.N. Synergistic Information Transfer in the Global System of Financial Markets. Entropy 2020, 22, 1000.

AMA Style

Scagliarini T, Faes L, Marinazzo D, Stramaglia S, Mantegna RN. Synergistic Information Transfer in the Global System of Financial Markets. Entropy. 2020; 22(9):1000.

Chicago/Turabian Style

Scagliarini, Tomas, Luca Faes, Daniele Marinazzo, Sebastiano Stramaglia, and Rosario N. Mantegna. 2020. "Synergistic Information Transfer in the Global System of Financial Markets" Entropy 22, no. 9: 1000.

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