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 insurance industry in Europe is facing the immediate and growing financial impacts of climate change. It advocates for a comprehensive and collaborative approach to climate resilience, stressing the foundational importance of emissions reduction, robust prevention measures, and a proactive funding model. The industry emphasizes that effective solutions must be tailored to local contexts and require strong leadership and financial commitment from public authorities in collaboration with the private sector.
L’Autorité des marchés financiers (AMF) et le Bureau du surintendant des institutions financières (BSIF) ont publié un rapport issu de l’Exercice normalisé d’analyse de scénarios climatiques (ENASC) 2024, impliquant plus de 250 institutions financières canadiennes. Bien que les risques climatiques ne posent pas de menace immédiate au secteur, ils pourraient s’intensifier à long terme, révélant des vulnérabilités. L’exercice a permis d’évaluer les risques physiques et de transition, et de renforcer leur mesure. Le rapport préconise d’améliorer les données, les modélisations et l’intégration de ces risques dans les processus décisionnels. Les conclusions influenceront les attentes de surveillance des deux organismes.