Nos derniers articles

We explore the challenge of measuring causal effects in empirical analyses, particularly in areas like asymmetric information and risk management. It emphasizes the importance of causal analysis in policy evaluation and discusses various frameworks such as instrumental variable, difference-in-differences, and generalized method of moments. The analysis addresses questions related to risk management's impact on firm value, moral hazard in insurance data, separating moral hazard from adverse selection, and the causal relationship between liquidity creation and reinsurance demand. The findings suggest that appropriate methodologies can enhance the value of risk management in firms despite residual information problems in various markets.

#bayesian data imputation holds significant importance in a variety of fields including #riskmanagement. Incomplete or missing data can hinder a thorough analysis of risks, making accurate decision-making challenging. By employing imputation techniques to fill in the gaps, risk managers can obtain a more comprehensive and reliable understanding of the underlying risk factors. This, in turn, enables them to make informed decisions and develop effective strategies for #riskmitigation.

The rise of generative AI and chatbots has brought Artificial General Intelligence (AGI) closer. The EU AI Act mentions general-purpose AI systems. While technical and ethical challenges in AGI are debated, organizational risk management is crucial. This paper suggests using LLCs as business entities for AGI systems to mitigate investor risks and promote AGI businesses through vertical and horizontal liability shields.