Nos derniers articles

The paper presents a dual-model framework for chaotic inference and rare-event detection. Model A, using Poincaré–Mahalanobis, focuses on geometric structure for stable inference. Model B, employing Correlation–Integral with Fibonacci diagnostics, emphasizes recurrence statistics and volatility clustering. The Lorenz–Lorenz experiments show that diagnostic weighting shifts inference from stability to rare-event focus. The Lorenz–Rössler experiments demonstrate Model B’s generalization across attractors, maintaining sensitivity to volatility. The framework combines stable geometric anchoring with robust rare-event detection, advancing systemic risk analysis. Future work aims to extend the models to higher-dimensional systems, optimize computational efficiency, and apply them to finance, climate, and infrastructure.

The study finds that banks engaging in greenwashing practices contribute to increased systemic risk, especially larger and less efficient ones. The market values actual ESG performance more than disclosures, and strong environmental performance helps mitigate this risk.

Effective risk management requires understanding aggregate risks, individual business unit riskiness, and systemic risks. Realistic models must consider complex phenomena like heterogeneous marginals and excess kurtosis. A modified individual risk model using Multivariate Stable Distributions addresses these challenges, enabling tractable aggregation, dependence analysis, and Tail Conditional Expectation calculations for aggregate risks.