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EIOPA's April 2025 Insurance Risk Dashboard indicates stable, medium-level risks in the European insurance sector, though pockets of vulnerability exist due to geopolitical uncertainty and market volatility. Macroeconomic risks are stable but with concerning GDP growth and inflation forecasts. Credit risks remained stable until early April, when spreads widened slightly. Market risks are elevated due to bond and equity volatility. Liquidity, solvency, profitability, financial interlinkages, and insurance risks are stable. Market sentiment is medium risk, and ESG risks are steady but with an intensifying outlook due to shifting environmental agreements.

This study introduces a novel capital allocation mechanism for banks, using game theory to assign capital requirements while enforcing macro-prudential standards. Based on competition for lower requirements, the approach employs insensitive risk measures from Chen et al. (2013) and Kromer et al. (2016), typically yielding a unique Nash allocation rule, while sensitive measures from Feinstein et al. (2017) may need additional conditions for uniqueness. The Eisenberg-Noe (2001) clearing system is analyzed for systemic risk, with numerical Nash allocations demonstrated. The study claims that further investigation into properties like continuity, monotonicity, or convexity is needed, noting that not all can hold simultaneously due to firm interactions.

“This update is based on the EBA reporting framework version 4.0 and covers indicators on institutions' profitability, solvency and operational risk, among others. The update also includes a new sets of risk indicators laid down in the Banking Package (Capital Requirements Regulation and Capital Requirements Directive - CRR3/CRD6), indicators related to Environmental, Social and Governance (ESG), and those already used in the context of the Minimum Requirement for Own Funds and Eligible Liabilities (MREL).”

An agent with multiple loss models optimizes risk sharing with a counterparty using a mean-variance criterion adapted for ambiguity. Under a Cramér-Lundberg loss model, the optimal risk sharing contract and wealth process are characterized. The strategy is proven admissible, and the value function verified. The optimal strategy is applied to Spanish auto insurance data with differing models from cross-validation for numerical illustrations.

En 2024, la France vit plus que jamais dans une « société du risque» face aux tensions géopolitiques, au décrochage économique européen et à l'aggravation des risques climatiques (année la plus chaude, événements naturels coûteux). Les Français se sentent vulnérables et inquiets face aux risques de guerre et à la capacité future d'assurer les risques climatiques et autres. Le secteur de l'assurance, bien que créateur d'emplois et gérant un grand nombre de sinistres (dont le coût des événements naturels a atteint 5 milliards d'euros en France), fait face à une hausse de la sinistralité (dégâts des eaux, sinistres graves pour les professionnels, cyberattaques, sinistralité agricole record) et des coûts (réparation automobile, dépenses de santé).

The banking industry faces complex financial risks, including credit, market, and operational risks, requiring a clear understanding of the aggregate cost of risk. Advanced AI models complicate transparency, increasing the need for explainable AI (XAI). Understanding risk mathematics enhances predictability, financial management, and regulatory compliance in an evolving landscape.

This work presents a framework for constructing elicitable risk measures with properties like monotonicity, translation invariance, and convexity using multiplicative scoring functions. It defines necessary conditions for these properties and provides a method for developing new elicitable functionals, with applications in finance, statistics, and machine learning.

This paper examines the Solvency II correlation matrix used in Solvency Capital Requirement (SCR) calculations. It warns against misinterpreting null correlations as independence and highlights the matrix's limitations without a well-defined probabilistic model. It also critiques the flawed practice of arbitrarily increasing correlations to inflate capital requirements conservatively.

The paper explores Pareto optimality in decentralized peer-to-peer risk-sharing markets using robust distortion risk measures. It characterizes optimal risk allocations, influenced by agents' tail risk assessments. Using flood risk insurance as an example, the study compares decentralized and centralized market structures, highlighting benefits and drawbacks of decentralized insurance.

Elicitable functionals and consistent scoring functions aid in optimal forecasting but assume correct distributions, which is unrealistic. To address this, robust elicitable functionals account for small misspecifications using Kullback-Leibler divergence. These robust functionals maintain statistical properties and are applied in reinsurance and robust regression settings.