63 résultats pour « Quantification des risques »

Multivariate Poisson Model Adjusting for Unidirectional Covariate Misrepresentation

"Insurance fraud has been a long-lasting issue in actuarial modeling. Policyholders are prone to hide their true status in their best interest when disclosing their information for insurance pricing purposes. However, from the insurers' point of view, it is either time-consuming or laborious to verify the true status of such risk factors. There is thus a strong incentive to build models accounting for potential misrepresentation, which contributes to a more robust ratemaking system."

The Road Less Travelled: Keynes and Knight on Probability and Uncertainty

"The possibilities of a Keynesian-Knightian synthesis as a way forward are considered by comparing these signposts. It is argued that, although there is some common ground between Knight and Keynes, there are fundamental differences particularly associated with Keynes’s concept of weight of argument."

Spatial Distance and Risk Category Effects in Enterprise Risk Management Practice

"... we find the difference in decision-makers’ probability assessments between operational and non-operational risk factors is greater when assessing a proximate rather than a remote target. We contribute to the accounting literature by demonstrating how spatial distance affects probability judgments."

A Conditional One‑Output Likelihood Formulation for Multitask Gaussian Processes

"Experimental results over synthetic and real problems confirm the advantages of this inference approach in its ability to accurately recover the original noise and signal matrices, as well as the achieved performance improvement in comparison to other state of art MTGP approaches."

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."