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Detecting Network Instability via Multiscale Detrended Cross-Correlations and MST Topology

Author

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  • Jose De Leon Miranda
  • Marina Dolfin
  • George Kapetanios
  • Leone Leonida

Abstract

We introduce a multiscale measure of network instability based on the joint use of Detrended Cross-Correlation Analysis (DCCA) and Minimum Spanning Tree (MST) filtering. The proposed metric, the Elastic Detrended Cross-Correlation Ratio (Elastic DCCR), is defined as a finite-difference measure of the logarithmic sensitivity of the average MST length to the observation scale. It captures how the structure of cross-correlation networks deforms across different investment horizons. When applied to a network of global equity indices, the Elastic DCCR rises sharply during episodes of financial stress, reflecting increased short-term coordination among investors and a contraction of correlation distances. The measure reveals scale-dependent reconfigurations in network topology that are not visible in single-scale analyses, and highlights clear differences between stressed and stable market regimes. The approach does not assume covariance stationarity and relies only on scale-dependent detrended correlations; as a result, it is broadly applicable to other complex systems in which interaction strength varies with scale.

Suggested Citation

  • Jose De Leon Miranda & Marina Dolfin & George Kapetanios & Leone Leonida, 2026. "Detecting Network Instability via Multiscale Detrended Cross-Correlations and MST Topology," Papers 2602.10174, arXiv.org.
    • Handle: RePEc:arx:papers:2602.10174
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