Weighted Signed Networks Reveal Interactions between US Foreign Exchange Rates
Leixin Yang1, Haiying Wang1, Changgui Gu1
1Business School, University of Shanghai for Science and Technology, Shanghai 200093, China.
Entropy (Basel, Switzerland)
|February 23, 2024
Summary
Exchange rate correlations reveal global financial shifts. Positive links grew with global connectivity, while negative correlations became rare, simplifying fluctuation patterns over three decades.
Area of Science:
- Economics
- Network Science
- Financial Markets
Background:
- Exchange rate correlations are key indicators of international trade and national financial dynamics.
- Understanding these correlations is crucial for analyzing global economic interactions.
Purpose of the Study:
- To analyze the evolving interactions within the US foreign exchange market.
- To identify changing patterns in currency correlations and their implications for global finance.
Main Methods:
- Detrended Cross-Correlation Analysis (DCCA) to quantify correlations.
- Optimization of time scale parameters for robust comparison.
- Construction of weighted signed networks to visualize relationships.
- Network centrality analysis and fluctuation propagation algorithms.
Main Results:
- Negative cross-correlations have significantly decreased over the last 30 years.
- Positive cross-correlations have increased in number and strength, reflecting greater global interconnectivity.
- Europe remains a financial center, with the euro and Danish krone showing stable relationships; emerging links with Asia noted since 2010.
- Fluctuation propagation patterns in exchange rate networks have become simpler and more predictable over time.
Conclusions:
- The study provides a novel method for analyzing exchange rate dynamics using DCCA and network analysis.
- Findings indicate a trend towards increased positive interdependence in global foreign exchange markets.
- The research offers insights into the evolving structure of international finance and currency interactions.
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