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Related Experiment Video

Updated: Jun 27, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Coupling Divergence Under Regime Switching: A Methodology for Structural Systemic Risk in Heterogeneous Subsystems.

Marin Pamukov¹, Nikolay Hinov^1,2

¹CoE "National Center of Mechatronics and Clean Technologies", 1000 Sofia, Bulgaria.

Entropy (Basel, Switzerland)

|June 26, 2026

Summary

This study introduces a novel framework to measure structural divergence between system states using matrix relative entropy. The method effectively distinguishes between different system regimes, offering insights beyond scalar measures.

Keywords:

Gaussian copula coupling divergence econophysics eigenbasis rotation heterogeneous subsystems hidden Markov model matrix relative entropy regime switching structural entropy systemic risk

Related Experiment Videos

Last Updated: Jun 27, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Area of Science:

Quantitative Finance
Systemic Risk Analysis
Statistical Modeling

Background:

Existing methods for systemic risk in heterogeneous systems (e.g., composite stress indices, spectral entropy) do not directly quantify structural divergence between conditional coupling matrices.
A gap exists in measuring regime-specific structural changes within complex multi-subsystem settings using explicit hidden-regime models.

Purpose of the Study:

To develop a novel framework for measuring structural divergence between regime-conditional coupling matrices in heterogeneous multi-subsystem settings.
To introduce a new metric, coupling divergence, based on matrix relative entropy within a hidden Markov process.

Main Methods:

Embedded whitened subsystem indicators within a two-regime Gaussian-copula hidden Markov process.
Defined coupling divergence as the matrix relative entropy between regime-conditional correlation matrices.
Established mathematical properties including non-negativity, reduction to scalar Kullback-Leibler divergence under commutativity, and orthogonal invariance.

Main Results:

The framework successfully separated regime-switching from single-regime cases in simulations within an operating window of T ∈ [250, 1000].
Isolated unique eigenbasis-rotation signals missed by sorted-eigenvalue methods, with significant divergence attributed to the non-commutative component.
Demonstrated tolerance to Gaussian-copula misspecification under heavy-tailed processes and identified expectation-maximization convergence as a diagnostic tool.

Conclusions:

The proposed framework quantifies regime-to-regime structural divergence and identifies a compositional mode of regime change beyond scalar methods.
The study's findings are based on internal validity claims using synthetic data; external validation on real-world multi-subsystem data remains an open area for future research.