“… stock market investors either do not treat the ESG score as a reliable measure of ESG performance or, embracing the “overinvestment view” rather than the “risk mitigation view” of Corporate Social Responsibility, do not associate positive ESG performance to greater corporate transparency and trustworthiness.”
“… relying on microprudential regulation alone would not be enough to account for the systemic dimension of transition risk. Implementing macroprudential policies in addition to microprudential regulation, leads to a Pareto improvement.”
“We lay a theoretical foundation for the choice of an exponential–Pareto combined distribution to model the severity of the operational risk. We derive, on a theoretical basis, the functional form of the operational risk severity distribution. The resulting loss severity distribution, in theory, is consistent with the parametric distribution that previous empirical works suggest is the best fit for loss data.”
Climate change presents substantial risks to global finance, yet current methodologies for quantifying these risks are often incomplete or misleading due to complexity. Challenges include data quality, model uncertainty, and integration into risk management frameworks. Improved models are needed to accurately assess climate risks and inform stakeholders for coherent decision-making.
Online transaction fraud poses significant challenges to businesses and consumers, with rule-based systems struggling to keep up. Machine learning, particularly personalized PageRank (PPR), offers promise by analyzing account relationships. Results show PPR enhances fraud detection models, providing valuable insights and stable features across datasets, improving predictive power.
The paper investigates two topics in game theory and decision-making. In the first part, it explores the concept of delegation within a Bayesian persuasion framework. In the second part, the paper focuses on the process of equilibrium selection between the Pareto dominant equilibrium and the risk dominant equilibrium.
This paper outlines the need for AI risk regulation due to documented harms caused by AI systems. It cites examples of proposed and enacted laws aimed at mitigating these risks but highlights challenges in quantifying harms. It criticizes a bias towards technocorrectionism and advocates for a broader regulatory approach to address AI's impacts effectively.
"... the level of insurance knowledge does not influence insurance risk attitudes but does influence the purchase of non-mandatory insurance products."
Analyzing EEA insurers from 2012 to 2021 using a difference-in-differences approach, this study reveals improvements in analysts' forecasts post-Solvency II implementation. Although no change in forecast bias is observed, there is a reduction in absolute earnings forecast errors and forecast dispersion, highlighting the positive impact of Solvency II disclosures on reporting accuracy. The findings contribute to insurance literature and inform regulatory authorities.
Experts agree on the societal benefits of pooling longevity risk through annuities and pensions. While pooling reduces the upfront capital needed for a secure income, challenges arise when participants vary in wealth and health. This paper proposes a model for distributing income in diverse longevity-risk pools, emphasizing the role of social cohesion.