. Furthermore, the model allows exact (without Monte Carlo simulation error) calculation of risk measures and their sensitivities … risk model CreditRisk+. This allows exact risk aggregation via an efficient numerically stable Panjer recursion algorithm … with respect to model parameters for P&L distributions such as value-at-risk and expected shortfall. Numerous examples …

in the computation of Value-at-Risk (VaR). Results show that copulas provide more sophisticated results in terms of the …

In the context of stochastic uncertainty and the increasing complexity of logistics processes in the retail sector … purpose of the present work is to assess the impact of risk events and unstable conditions on the level of quality of supply … chain services and economic indicators of the retail trade network. Using the anyLogistix software tool, a simulation model …

We present a stochastic simulation forecasting model for stress testing that is aimed at assessing banks’ capital …

ensures that the flexible risk response concept under conditions of improbable uncertainty is taken into account when …Modern conditions for real investment are generally associated with increasing uncertainty, which is even more relevant …, considering the project's uncertainty characteristics. The logic of the analysis methods transformation under the influence of a …

approaches for dealing with parameter uncertainty are discussed and simulation algorithms based on (pseudo-marginal) Sequential … calculated for the one-year risk with dependencies based on correlations given by the Swiss Solvency Test. Two different …

framework is model-free and allows for stresses on the output such as (a) the mean and variance, (b) any distortion risk measure … including the Value-at-Risk and Expected-Shortfall, and (c) expected utility type constraints, thus making the reverse … sensitivity analysis framework suitable for risk models. …