Nos derniers articles

The provided text is an **academic article** that offers a comprehensive **analytical review of cyber risk management** within the insurance industry, focusing heavily on the **mathematical models** used for risk quantification and premium pricing. The review systematically covers the current state-of-the-art in cyber risk, discussing how dynamic and interconnected threats challenge traditional actuarial methods, necessitating the use of advanced quantitative tools like **stochastic models and copulas** to manage dependencies and calculate **Solvency Capital Requirements (SCR)**. It thoroughly details various **vulnerability functions** (including the well-known Gordon-Loeb model and its extensions) and different **premium calculation principles** (such as Expected Value and Mean-Variance), concluding that closer collaboration between different disciplines is essential for developing **robust cyber insurance and reinsurance solutions** in an increasingly digital landscape.

The geospatial Agent-Based Model (ABM) framework outlined in this article enables financial institutions, including insurers, to quantify direct and cascading climate risks, capturing spatial and temporal dynamics and supply chain disruptions overlooked by traditional models. It supports climate scenario analysis for enhanced risk assessment and portfolio management, revealing systemic risks affecting even indirectly exposed agents. The framework evaluates cost-effective adaptation strategies, showing how firms’ adaptive behaviors, like pre-emptive capital increases, reduce climate impacts. By integrating geospatial climate data with economic models, it bridges gaps between climate projections and financial decision-making, aiding risk management and capital allocation.

This research addresses the critical challenge of model ambiguity in insurance, where the true probabilities of losses are uncertain. It introduces randomly distorted Choquet integrals, a novel mathematical tool for creating flexible and dynamic risk measures. This provides a formal, unified methodology to resolve expert disagreements by extending industry-standard metrics like Value at Risk (VaR) and Average Value at Risk (AVaR). The framework allows a decision-maker to synthesize divergent opinions—whether on key parameters like a VaR confidence level or on the fundamental risk model itself (e.g., VaR vs. AVaR)—into a single, coherent, and scenario-dependent assessment.

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 examines the relationship between ESG variables and financial risk, measured through logarithmic volatility. It introduces the Hierarchical Variable Selection (HVS) algorithm, designed for ESG datasets, which is reported to outperform aggregated ESG scores and traditional selection models by providing higher explanatory power with fewer variables. Findings suggest that ESG risk factors vary across sectors and between large- and small-cap firms, influenced by differences in regulation, expectations, and strategy. The authors highlight the robustness and adaptability of HVS, noting its effectiveness in identifying risk-relevant ESG variables across industries and its potential for broader applications in hierarchical datasets.

This paper analyzes a bivariate optimal dividend problem for an insurer with two collaborating business lines under a diffusion model with correlated Brownian motions. The framework incorporates dividend payouts, proportional reinsurance, and inter-line capital transfers to prevent bankruptcy. The authors provide complete analytical solutions, identifying three scenarios with closed-form value functions and optimal strategies. Results show a threshold dividend policy, with the more important line having a lower threshold. Optimal reinsurance decreases with aggregate reserves and stabilizes after a switching point. Correlation between lines affects reinsurance, and the capital transfer rule is consistent across scenarios.

The draft strengthens governance arrangements, clarifies management body roles, and enhances oversight of internal control, risk management, and compliance functions. It incorporates ICT and security risk management in line with DORA, requiring institutions to integrate digital operational resilience into governance frameworks. The revisions also address anti-money laundering, conflicts of interest, and gender-neutral remuneration. Stakeholders can submit feedback until October 2025, with final guidelines to replace the 2017 version.

This study explores how natural disasters challenge traditional risk management and insurance mechanisms. Researchers developed a three-strategy evolutionary game model to examine the competition among formal index insurance, informal risk sharing, and non-insurance. The model incorporates insurance company profits to aid optimal pricing. Findings suggest that basis risk and loss ratios strongly influence insurance adoption. Low basis risk and high loss ratios favor index insurance, while moderate loss ratios lead to informal risk sharing. Low loss ratios often result in no insurance uptake. Accurately estimating risk aversion and risk sharing ratios is essential for forecasting index insurance market trends.

This Final Report (EBA/RTS/2025/03) presents draft Regulatory Technical Standards (RTS) under the Capital Requirements Regulation (CRR) III. It addresses three mandates:
• An operational risk taxonomy with Level 1 event types, Level 2 categories and supplementary attributes (including ESG and ICT risks), to standardise how institutions classify loss events.
• Criteria for deeming the annual‑operational‑risk loss calculation “unduly burdensome” for certain institutions, allowing temporary waivers.
• Rules for adjusting loss‑data sets when firms merge or acquire entities, including currency conversion, re‑classification and fallback proxies.

This paper introduces an 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝘃𝗲 𝗵𝘆𝗯𝗿𝗶𝗱 𝗶𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲 𝗺𝗼𝗱𝗲𝗹 designed to cover 𝗵𝗲𝗮𝘃𝘆-𝘁𝗮𝗶𝗹𝗲𝗱 𝗹𝗼𝘀𝘀𝗲𝘀, which are extreme and potentially limitless financial damages, often associated with natural disasters. 𝗧𝗵𝗲 𝗺𝗼𝗱𝗲𝗹 𝗰𝗼𝗺𝗯𝗶𝗻𝗲𝘀 𝘁𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗶𝗻𝗱𝗲𝗺𝗻𝗶𝘁𝘆-𝗯𝗮𝘀𝗲𝗱 𝗶𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲 𝗳𝗼𝗿 𝘀𝗺𝗮𝗹𝗹𝗲𝗿 𝗹𝗼𝘀𝘀𝗲𝘀 𝘄𝗶𝘁𝗵 𝗽𝗮𝗿𝗮𝗺𝗲𝘁𝗿𝗶𝗰 (𝗶𝗻𝗱𝗲𝘅-𝗯𝗮𝘀𝗲𝗱) 𝗶𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲 𝗳𝗼𝗿 𝗹𝗮𝗿𝗴𝗲𝗿, 𝗰𝗮𝘁𝗮𝘀𝘁𝗿𝗼𝗽𝗵𝗶𝗰 𝗲𝘃𝗲𝗻𝘁𝘀. A key contribution is the development of a 𝘀𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗲𝗱 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗰𝗿𝗶𝘁𝗲𝗿𝗶𝗼𝗻 and a 𝘁𝘄𝗼-𝘀𝘁𝗲𝗽 𝗰𝗮𝗹𝗶𝗯𝗿𝗮𝘁𝗶𝗼𝗻 𝗺𝗲𝘁𝗵𝗼𝗱𝗼𝗹𝗼𝗴𝘆 that can leverage readily available covariate data, even when comprehensive loss data is scarce. Empirical analysis using both 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗲𝗱 𝗮𝗻𝗱 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘁𝗼𝗿𝗻𝗮𝗱𝗼 𝗱𝗮𝘁𝗮 demonstrates that 𝘁𝗵𝗶𝘀 𝗵𝘆𝗯𝗿𝗶𝗱 𝗰𝗼𝗻𝘁𝗿𝗮𝗰𝘁 𝗼𝘂𝘁𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝘀 𝘁𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗰𝗮𝗽𝗽𝗲𝗱 𝗶𝗻𝗱𝗲𝗺𝗻𝗶𝘁𝘆 𝗰𝗼𝗻𝘁𝗿𝗮𝗰𝘁𝘀 by providing better coverage for the same premium, especially benefiting regions with limited data. The authors highlight the practical advantages of 𝗳𝗮𝘀𝘁𝗲𝗿 𝗰𝗼𝗺𝗽𝗲𝗻𝘀𝗮𝘁𝗶𝗼𝗻 and 𝗿𝗲𝗱𝘂𝗰𝗲𝗱 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗰𝗼𝘀𝘁𝘀 offered by the parametric component.