75 résultats
pour « Quantification des risques »
Cyber risk presents significant challenges to society, yet its statistical behavior remains insufficiently understood. This paper analyzes three databases to study cyber risk dynamics. It identifies increasing frequency and severity, particularly in malicious events since 2018. Persistent heavy-tailedness across risk categories implies lower insurance demand and potentially heightened risk levels for firms.
The study explores optimal decision-making for agents minimizing risks with extremely heavy-tailed, possibly dependent losses. Focused on super-Pareto distributions, including heavy-tailed Pareto, it finds non-diversification preferred with well-defined risk measures. Equilibrium analysis in risk exchange markets indicates agents with such losses avoid risk sharing. Empirical data confirms real-world heavy-tailed distributions.
The objective of this paper is to compare the most common available Risk quantification models: Fault Tree Analysis, Failure Mode Effective Analysis, and FAIR (Factor Analysis of Information Risk) Model.
Operational risk modeling requires flexible distributions for non-negative values, particularly those exhibiting heavy-tail behavior. Composite or spliced models, like composite Tukey-type distributions, are gaining attention for their ability to handle extreme and ordinary observations effectively. This paper explores the flexibility of such distributions, offering empirical validation with operational risk data.
The Basel II advanced measurement approach often yields counterintuitive operational risk capital due to extreme loss events. To address this, the semi-nonparametric (SNP) model by Chen and Randall (1997) can enrich parametric model distributions. SNP shows improvement over parametric models, providing more intuitive capital estimates consistent with extreme value theory.
"Entropic Value-at-Risk (EVaR) ... was previously calculated explicitly only for the normal distribution. We succeeded ... to calculate EVaR for Poisson, compound Poisson, Gamma, Laplace, exponential, chi-squared, inverse Gaussian distribution and normal inverse Gaussian distribution with the help of Lambert function that is a special function, generally speaking, with two branches.”
“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.”
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.
The study introduces partial law invariance, a novel concept extending law-invariant risk measures in decision-making under uncertainty. It characterizes partially law-invariant coherent risk measures with a unique formula, deviating from classical approaches. Strong partial law invariance is introduced, proposing new risk measures like partial versions of Expected Shortfall for risk assessment under model uncertainty.
The paper introduces a new robust estimation technique, the Method of Truncated Moments (MTuM), tailored for estimating the tail index of a Pareto distribution from grouped data. It addresses limitations in existing methods for grouped loss severity data, providing inferential justification through the central limit theorem and simulation studies.