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Synergistic information transfer in the global system of financial markets

(2020) ENTROPY. 22(9).
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Abstract
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.
Keywords
information theory, time series analysis, econometrics, stock market

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MLA
Scagliarini, Tomas, et al. “Synergistic Information Transfer in the Global System of Financial Markets.” ENTROPY, vol. 22, no. 9, 2020, doi:10.3390/e22091000.
APA
Scagliarini, T., Faes, L., Marinazzo, D., Stramaglia, S., & Mantegna, R. N. (2020). Synergistic information transfer in the global system of financial markets. ENTROPY, 22(9). https://doi.org/10.3390/e22091000
Chicago author-date
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 (9). https://doi.org/10.3390/e22091000.
Chicago author-date (all authors)
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 (9). doi:10.3390/e22091000.
Vancouver
1.
Scagliarini T, Faes L, Marinazzo D, Stramaglia S, Mantegna RN. Synergistic information transfer in the global system of financial markets. ENTROPY. 2020;22(9).
IEEE
[1]
T. Scagliarini, L. Faes, D. Marinazzo, S. Stramaglia, and R. N. Mantegna, “Synergistic information transfer in the global system of financial markets,” ENTROPY, vol. 22, no. 9, 2020.
@article{8673975,
  abstract     = {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.},
  articleno    = {1000},
  author       = {Scagliarini, Tomas and Faes, Luca and Marinazzo, Daniele and Stramaglia, Sebastiano and Mantegna, Rosario N.},
  issn         = {1099-4300},
  journal      = {ENTROPY},
  keywords     = {information theory,time series analysis,econometrics,stock market},
  language     = {eng},
  number       = {9},
  pages        = {13},
  title        = {Synergistic information transfer in the global system of financial markets},
  url          = {http://dx.doi.org/10.3390/e22091000},
  volume       = {22},
  year         = {2020},
}

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