63 résultats
pour « Quantification des risques »
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.
This paper explores risk factor distribution forecasting in finance, focusing on the widely used Historical Simulation (HS) model. It applies various deep generative methods for conditional time series generation and proposes new techniques. Evaluation metrics cover distribution distance, autocorrelation, and backtesting. The study reveals HS, GARCH, and CWGAN as top-performing models, with potential future research directions discussed.
This research develops a mathematical model using Extreme Value Theory and Risk Measures to estimate and forecast major fire insurance claims, enhancing insurers' understanding of potential risks. Utilizing a three-parameter Generalized Pareto Distribution in the Extreme Value Theory framework, the study effectively models large losses, aiding in risk management and pricing strategies for insurance firms.