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Weighted network analysis of high-frequency cross-correlation measures.
1Department of Economics, City University, Northampton Square, London, EC1V 0HB, United Kingdom. g.iori@city.ac.uk
Summary
This study introduces a Fourier method for analyzing financial correlation matrices, offering a less noisy alternative to standard measures. This technique enhances the detection of subtle correlation changes using limited data, improving financial market analysis.
Area of Science:
- Quantitative Finance
- Network Science
- Statistical Analysis
Background:
- Estimating high-frequency correlation matrices from limited data is challenging using standard methods like Pearson correlation.
- Existing methods are often too noisy to detect subtle shifts in correlations, especially with small datasets.
Purpose of the Study:
- To implement and evaluate a novel Fourier method for estimating high-frequency correlation matrices.
- To demonstrate the effectiveness of the Fourier method in reducing noise compared to traditional measures.
- To analyze the evolution of correlations over various time scales using network theory.
Main Methods:
- Implementation of a Fourier-based method for correlation matrix estimation.
- Comparison of Fourier estimates with standard Pearson correlation measures.
- Application of measures from random weighted network theory to analyze correlation evolution.
- Utilizing minimum spanning tree representations of correlation matrices.
Main Results:
- Fourier estimates exhibit significantly lower noise levels compared to Pearson correlation measures.
- The proposed method can detect subtle changes in correlation matrices with as little as one month of data.
- Analysis revealed the evolution of correlations at different time scales.
Conclusions:
- The Fourier method provides a robust approach for estimating correlation matrices from small datasets.
- This technique offers improved sensitivity for detecting dynamic changes in financial markets.
- The integration of network theory provides deeper insights into correlation dynamics.
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