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Area of Science:

  • Computational Finance
  • Network Science
  • Information Theory

Background:

  • Predicting currency value fluctuations is crucial for financial markets.
  • Traditional methods often overlook complex nonlinear causal relationships between currencies.

Purpose of the Study:

  • To predict currency value fluctuations using information and network theory.
  • To analyze and model nonlinear causal relationships between 48 currencies over 25 years.

Main Methods:

  • Calculated causal relationships using logarithmic return (log-return) and entropic value-at-risk (EVaR).
  • Quantified causal relationships via transfer entropy.
  • Modeled and analyzed information flow as a network.
  • Classified currencies using hierarchical clustering.
  • Predicted fluctuations with machine learning based on network topology.

Main Results:

  • Information flow-based nonlinear causal relationships differ from the established key currency order.
  • Network analysis revealed distinct currency communities based on causal relationships.
  • Machine learning models showed improved currency fluctuation predictions using data from these communities.

Conclusions:

  • Statistically significant nonlinear causal relationships provide a novel basis for currency classification.
  • Leveraging network topology and information flow enhances data efficiency in currency prediction models.
  • This approach offers a more nuanced understanding of inter-currency dynamics.