69 résultats
pour « Quantification des risques »
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.
#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.
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.
This paper presents a fundamental #mathematical duality linking utility transforms and #probability distortions, which are vital in #decisionmaking under #risk. It reveals that these concepts are characterized by commutation, allowing for simple axiomatization with just one property. Additionally, rank-dependent utility transforms are further characterized under monotonicity conditions.
"This paper examines a #stochastic one-period #insurancemarket with incomplete information. The aggregate amount of #claims follows a compound #poisson distribution. #insurers are assumed to be exponential utility maximizers, with their degree of #riskaversion forming their private information. A premium strategy is defined as a map between risk types and premium rates. The optimal premium strategies are denoted by the pure-strategy #bayesian #nash equilibrium, whose existence and uniqueness are demonstrated under specific conditions for the demand function..."
This study examines interpersonal heterogeneity in #risk attitudes in #decisionmaking experiments. The use of #bayesian and classical methods for estimating the hierarchical model has sparked debate. Both approaches use the population distribution of risk attitudes to identify individual-specific risk attitudes. Comparing existing experimental data, both methods yield similar conclusions about risk attitudes.
#bayesian data imputation is a technique used to fill in missing data in a variety of fields, including #riskmanagement. By employing imputation techniques to fill in the gaps, #riskmanagers can obtain a more comprehensive and reliable understanding of the underlying #risk factors, enabling them to make informed decisions and develop effective strategies for #riskmitigation.
"#expectile is a #risk measure that, similar to #var (quantile) and CVaR (superquantile), can be employed in #riskmanagement."
"This paper introduces the multivariate range Value-at-Risk (MRVaR) and multivariate range covariance (MRCov) as #risk#measures for #riskmanagement in #regulation and investment… Frequently-used cases in industry, such as normal, student-t, logistic, Laplace, and Pearson type VII distributions, are presented with numerical examples."