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Updated: Mar 24, 2026

Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment
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Interdependencies and Causalities in Coupled Financial Networks.

Irena Vodenska1,2, Hideaki Aoyama3, Yoshi Fujiwara4

  • 1Metropolitan College, Boston University, 808 Commonwealth Avenue, Boston, MA 02215, United States of America.

Plos One
|March 16, 2016
PubMed
Summary

Foreign exchange markets predict global stock market performance. Complex Hilbert principal component analysis reveals lead-lag relationships, with the US, Germany, and Mexico showing significant forecasting power.

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Area of Science:

  • Economics
  • Network Science
  • Financial Markets

Background:

  • Previous studies focused on limited country groups (e.g., G5, G7).
  • A global perspective is needed to understand intermarket dynamics.
  • Financial crises significantly impact market synchronization.

Purpose of the Study:

  • To model lead-lag relationships between foreign exchange and stock markets globally.
  • To identify stable network communities within these markets.
  • To analyze market dynamics across different economic periods (mild crisis, calm, severe crisis).

Main Methods:

  • Complex Hilbert Principal Component Analysis (CH-PCA)
  • Coupled synchronization network construction
  • Community analysis

Main Results:

  • Identified four stable, distinct network communities across 48 countries.
  • Severe crisis periods (2007-2012) showed dominant dynamics in the synchronization network.
  • Foreign exchange markets generally predict global stock market performance.

Conclusions:

  • The foreign exchange market possesses predictive power for global equity markets.
  • The United States, German, and Mexican markets exhibit significant forecasting capabilities for other global equity markets.
  • CH-PCA is effective for uncovering lead-lag relationships in complex financial networks.