67 résultats pour « Quantification des risques »

Evaluation of Backtesting on Risk Models Based on Data Envelopment Analysis

"The methodologies examined include filtered historical simulation, extreme value theory, Monte Carlo simulation and historical simulation. Autoregressive-moving-average and generalized-autoregressive-conditional-heteroscedasticity models are used to estimate VaR."

Modeling Multivariate Operational Losses Via Copula‑Based Distributions with G‑and‑H Marginals

"The empirical evidence suggests that a distribution based on a single copula is not flexible enough, and thus we model the dependence structure by means of vine copulas. We show that the approach based on regular vines improves the fit. Moreover, even though losses corresponding to different event types are found to be dependent, the assumption of perfect positive dependence is not supported by our analysis. "

Uncertainty Quantification of Multi‑Scale Resilience in Nonlinear Complex Networks

" In order to quantify resilience uncertainty across the network resolutions (from macro-scale network statistics to individual node dynamics), we propose an arbitrary polynomial chaos (aPC) expansion method to identify the probability of a node in losing its resilience and how the different model parameters contribute to this risk on a single node."

Ruin Problems in a Discrete Risk Model in a Markovian Environment

"In a 2019 paper, Yang, Zhang and Lan studied a risk model in which claim occurrence and amount are both governed by an underlying Markovian environment process. We find that the derivations of Yang et al are erroneous; subsequently, we analyzed the model correctly using the matrix analytic method."

Pareto‑optimal Reinsurance Under Individual Risk Constraints

"This paper studies the design of Pareto-optimal reinsurance contracts in a market where the insurer and reinsurer maximize their expected utilities of end-of-period wealth. In addition, we assume that the insurer and reinsurer wish to control their solvency risks, which are defined through distortion risk measures of their end-of-period risk exposures."

A Bayesian‑Loss Function Model for Assessing Marine Liability Regime for Ship‑Source Spills

"The model is a comprehensive template for assessing loss and subsequently the insurance for activities in the Arctic and sub-Arctic regions. Governmental and non-government organisations alike will benefit from the tool by using it as a loss estimation mechanism for liability for ship-source oil spills."