159 résultats
pour « riskmanagement »
" In quantifying the solvency capital requirement gradient for cyber risk measurement according to Solvency II, a dangerous paradox emerges: an insurance company can be ranked as solvent according to Pillar 1 without adequately evaluating the operational solvency capital requirements under Pillar 2. "
"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."
"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. "
"The standard statistical approaches to assessment of insurability and potential mispricing are enhanced in several aspects involving consideration of model risk … We demonstrate how to quantify the effect of model risk in this analysis by incorporating various robust estimators for key model parameter estimates that apply in both marginal and joint cyber risk loss process modelling."
"... we find that the Bayesian approach outperforms the classical one in identifying whether a model is correctly specified which is the principal aim of any backtesting framework. The power of the methodology is due to its ability to test individual model parameters and hence identify which aspects of a model are misspecified as well as the degree of misspecification."
"This document explains the enterprise risk management framework in a flow chart form."
"This document is showing the flow chart of life insurance risk"
"We portray the distributions that are fundamental for enterprise risk management and describe when they can be used. These include the Bernoulli distribution, the binomial distribution, the Poisson distribution, the uniform distribution, the triangular distribution, the PERT distribution, the modified PERT distribution, the trapezoidal distribution, the custom distribution, the normal distribution, the lognormal distribution, the Weibull distribution and the compound distribution."
"Neural networks are suggested for learning a map from d-dimensional samples with any underlying dependence structure to multivariate uniformity in d′ dimensions."
"Neural networks are suggested for learning a map from d' dimensional samples with any underlying dependence structure to multivariate uniformity in d′ dimensions."