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.

Accurate insurance claims forecasting is vital for financial planning and risk management. This study introduces innovative variables, such as weather conditions and car sales, and employs Machine Learning algorithms to predict average insurance claims per quarter. Key influential variables include new car sales and minimum temperature with specific lags. The findings aid insurers in enhancing claims forecasting by considering additional parameters like weather and sales data.

We study risk processes with level dependent premium rate. Assuming that the premium rate converges, as the risk reserve increases, to the critical value in the net-profit condition, we obtain upper and lower bounds for the ruin probability. In contrast to existing in the literature results, our approach is purely probabilistic and based on the analysis of Markov chains with asymptotically zero drift.

The study exploits Basel III's sequential adoption and ultimate parent rule, creating two bank groups under different regulations in the same country: early-adopting subsidiaries (treated banks) and domestic banks (untreated). Using a difference-in-difference approach, it empirically identifies Basel III effects by comparing risk and performance changes before and after the 2015 implementation in non-adopting countries.

#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.

Proactive cyber-risk assessment is gaining importance due to its potential benefits in preventing cyber incidents across various sectors and addressing emerging vulnerabilities in cyber-physical systems. This study presents a robust statistical framework, using mid-quantile regression, to assess cyber vulnerabilities, rank them, and measure accuracy while dealing with partial knowledge. The model is tested with simulated and real data to support informed decision-making in operational scenarios.

“The #eu draft for an #euaiact is a legal medley. Under the banner of #risk-based #regulation, the AI Act combines two repertoires of EU law, namely #productsafety and #fundamentalrights protection. However, the proposed medley can fail if it does not account for the structural differences between the two legal repertoires…”

This paper introduces new characterizations for certain types of law-invariant star-shaped functionals, particularly those with stochastic dominance consistency. It establishes Kusuoka-type representations for these functionals, connecting them to Value-at-Risk and Expected Shortfall. The results are versatile and applicable in diverse financial, insurance, and probabilistic settings.

“Gaps in the data available for assessing cyber risk have limited the development of metrics that would help the public and private sectors prevent and recover from cyberattacks and reduce systemic risk. Cyber incident disclosure rules, introduced to close the data gaps, help but fall short in supporting the effective management of cyber risk. This article examines current and proposed reporting requirements, especially in the financial sector, where they are the most advanced.”

ESG scores and climate policy uncertainty affect default risk in ESG and non-ESG firms. The study uses various metrics and machine learning models to analyze default risk over 20 years, offering policy insights for risk management in corporations and government.