This paper argues that traditional cyber risk classifications are too restrictive for effective out-of-sample forecasting. It recommends focusing on dynamic, impact-based classifications for better predictions of cyber risk losses, suggesting that risk types are more useful for modeling event frequency rather than severity.
This paper explores moral hazard in insurance when individuals test for risk severity. It highlights how regulations and loss reduction costs impact behavior. Monetary costs lead to uniform loss reduction, while convex costs drive higher-risk individuals to reduce losses more. Insurers can incentivize risk discovery and reduction through tailored contracts.
This paper adapts Gouriéroux and Monfort's (2021) model risk framework to property and casualty insurance, focusing on policy-level data. It addresses model risk at two levels: the impact on predictions and out-of-sample uncertainty, and the need to account for risk during model selection.
This paper explores optimal insurance contracting for a decision maker facing ambiguous loss distributions. Using a p-Wasserstein ball around a benchmark distribution and a convex distortion risk measure, the indemnity function and worst-case distribution are derived. Numerical examples highlight the sensitivity of worst-case distributions to model parameters.Distributionally robust insurance under the Wasserstein distance
This research examines how ESG performance impacts default probability (PD) in life and non-life insurance firms. Findings show that improved ESG practices reduce both short-term and long-term PD, benefiting credit ratings and financial stability. Policymakers and managers can use this to enhance risk management and sustainable finance strategies.
The study assesses the impact of Europe's Single Supervisory Mechanism on banks' balance sheets, finding that centrally supervised banks have higher Tier 1 capital ratios. This is influenced by capital requirements, business models, and credit risk, particularly in countries with less stringent regulations, leading to increased resilience.
This study assesses flood-related financial stability risks in the Netherlands through diverse scenarios for bank stress tests. Results show varying impacts on bank capital, amplified by climate change. Stronger defenses can mitigate some effects, and there's a non-linear relationship between flood damages and capital depletion, emphasizing extreme scenario consideration.
This research uses Monte Carlo simulations to examine managing basis risk in parametric insurance through diversification. Findings show that increasing contracts reduces risk and volatility, spatial relationships significantly affect risk levels, and disaster severity has little impact, suggesting severe events shouldn't limit parametric insurance development.
This article reviews the EU's Artificial Intelligence Act, highlighting its structure, scope, and key principles like fairness and transparency. It critiques the complexity of regulating high-risk AI, forbidden practices, and the risk of hindering responsible innovation despite an overall balanced framework.
This paper addresses actuarial challenges in insurance by developing a user-friendly algorithm for optimal reinsurance decisions, balancing capital efficiency and asset/liability management. It combines expert judgment with quantitative methods, overcoming computational barriers for non-specialists. The techniques can be applied to broader risk management problems in insurance.