. 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 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 …

measures of risk. Furthermore, Qα(X ; p) is the optimal value in a certain minimization problem, the minimizers in which are … problems. In finance, Q0(X;p) and Q1(X ; p) are known as the value at risk (VaR) and the conditional value at risk (CVaR). The … sensitivity to risk. The problems of the effective computation of the bounds are considered. Various other related results are …

that Lévy process-based models provide a better fit to the US statutory accounting data, and identifies how parameter risk … insurance parameter risk. …

We study risk-minimization for a large class of insurance contracts. Given that the individual progress in time of …-Kunita-Watanabe decomposition for a general insurance contract and specify risk-minimizing strategies in a Brownian financial market setting. The …

. The primary insurer’s point of view is documented in terms of assessment of risk and payment of reinsurance premium. A … reinsurance. Assuming the classical compound Poisson risk model with choices of claim size distributions (classified as heavy … LCR reinsurance than for EOL while still maintaining company value. Both methods reduce risk considerably as compared with …

, enabling us to study parameter uncertainty and the risk related to the estimation procedure (estimation risk). In particular …, we use the distribution of empirical parameters from IBM and EURO STOXX50. The evidence suggests that estimation risk …