Nos derniers articles

"We find that depending on the capitalisation of the network, a holding structure can be beneficial as compared to smaller separated entities. In other instances it can be harmful and actually increase contagion. We illustrate our results in a numerical case study and also determine the optimal level of holding support from a regulator perspective."

"... results are applied in a wide range of actuarial problems including multivariate risk measures, aggregate loss, large claims reinsurance, weighted premium calculations and risk capital allocation. "

"From a supervisory perspective, the use of AI can be expected to decrease regulatory enforcement costs while providing technology-advanced players with opportunities to game the regulatory system."

"Financial supervisors as well as financial intermediaries increasingly rely on AI. However, little remains known about the scope and pervasiveness of this evolution."

"We extend the scope of risk measures for which backtesting models are available by proposing a multinomial backtesting method for general distortion risk measures. The method relies on a stratification and randomization of risk levels. We illustrate the performance of our methods in numerical case studies."

"I argue that that conventional risk analysis—meaning risk analysis fixated on controlling risks—should expand to systematically integrate two related principles. The first is prevention, which seeks in the first instance to avoid the risk altogether. The second is resilience, which aims build the capacity to respond to whatever does come to pass."

" The introduced valuation principle relies on stochastic ordering so that the valuation risk-loading, and thus risk premiums, generated by the measure distortion is an ordered parametric family. The quantile processes are generated by a composite map consisting of a distribution and a quantile function."

"We develop an approach for solving time-consistent risk-sensitive stochastic optimization problems using model-free reinforcement learning (RL). Specifically, we assume agents assess the risk of a sequence of random variables using dynamic convex risk measures. We employ a time-consistent dynamic programming principle to determine the value of a particular policy, and develop policy gradient update rules. We further develop an actor-critic style algorithm using neural networks to optimize over policies. Finally, we demonstrate the performance and flexibility of our approach by applying it to optimization problems in statistical arbitrage trading and obstacle avoidance robot control."

"... insurance carriers should internally organize key stakeholders related to AI strategy and development to collaboratively evaluate how they define and develop AI projects and models. If carriers have not yet established broad life cycle governance or risk management practices unique to their AI/machine learning systems, they should begin that journey with haste."